|
Please note that this page does not hosts or makes available any of the listed filenames. You
cannot download any of those files from here.
|
| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
| 0 |
16B |
| 01__MentorProgramInformation.pdf |
62.75KB |
| 01__resources.html |
4.04KB |
| 01_1-nearest-neighbor-regression-approach.en.srt |
9.73KB |
| 01_1-nearest-neighbor-regression-approach.en.txt |
6.04KB |
| 01_1-nearest-neighbor-regression-approach.mp4 |
21.48MB |
| 01_a-brief-recap.en.srt |
2.39KB |
| 01_a-brief-recap.en.srt |
2.38KB |
| 01_a-brief-recap.en.srt |
3.95KB |
| 01_a-brief-recap.en.srt |
1.57KB |
| 01_a-brief-recap.en.txt |
2.43KB |
| 01_a-brief-recap.en.txt |
1.47KB |
| 01_a-brief-recap.en.txt |
1.50KB |
| 01_a-brief-recap.en.txt |
1.01KB |
| 01_a-brief-recap.mp4 |
6.37MB |
| 01_a-brief-recap.mp4 |
5.05MB |
| 01_a-brief-recap.mp4 |
4.42MB |
| 01_a-brief-recap.mp4 |
3.91MB |
| 01_acknowledgements_instructions.html |
5.30KB |
| 01_acknowledgments_instructions.html |
5.30KB |
| 01_acknowledgments_instructions.html |
5.29KB |
| 01_adaboost-overview.en.srt |
3.99KB |
| 01_adaboost-overview.en.txt |
2.37KB |
| 01_adaboost-overview.mp4 |
7.75MB |
| 01_advanced-optimization.en.srt |
10.54KB |
| 01_advanced-optimization.en.txt |
5.56KB |
| 01_advanced-optimization.mp4 |
15.57MB |
| 01_alternatives-to-the-sigmoid-activation.en.srt |
6.83KB |
| 01_alternatives-to-the-sigmoid-activation.en.txt |
4.31KB |
| 01_alternatives-to-the-sigmoid-activation.mp4 |
11.96MB |
| 01_analyzing-product-sentiment-assignment_amazon_baby.csv |
85.30MB |
| 01_analyzing-product-sentiment-assignment_amazon_baby.sframe.zip |
40.33MB |
| 01_analyzing-product-sentiment-assignment_datastructures.html |
93.13KB |
| 01_analyzing-product-sentiment-assignment_FND03-NB01.ipynb.zip |
13.08KB |
| 01_analyzing-product-sentiment-assignment_instructions.html |
17.25KB |
| 01_analyzing-product-sentiment-assignment_turicreate.SArray.apply.html |
12.71KB |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.srt |
50.57KB |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.en.txt |
31.74KB |
| 01_andrew-ng-and-chelsea-finn-on-ai-and-robotics.mp4 |
230.66MB |
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.srt |
63.95KB |
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.en.txt |
39.86KB |
| 01_andrew-ng-and-chris-manning-on-natural-language-processing.mp4 |
236.15MB |
| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.srt |
59.69KB |
| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.en.txt |
30.68KB |
| 01_andrew-ng-and-fei-fei-li-on-human-centered-ai.mp4 |
214.72MB |
| 01_a-performance-metric-for-recommender-systems.en.srt |
7.02KB |
| 01_a-performance-metric-for-recommender-systems.en.txt |
4.38KB |
| 01_a-performance-metric-for-recommender-systems.mp4 |
16.52MB |
| 01_application-of-deep-learning-to-computer-vision.en.srt |
7.83KB |
| 01_application-of-deep-learning-to-computer-vision.en.txt |
4.92KB |
| 01_application-of-deep-learning-to-computer-vision.mp4 |
19.31MB |
| 01_balancing-fit-and-magnitude-of-coefficients.en.srt |
7.61KB |
| 01_balancing-fit-and-magnitude-of-coefficients.en.txt |
4.67KB |
| 01_balancing-fit-and-magnitude-of-coefficients.mp4 |
18.11MB |
| 01_boosting-a-decision-stump_CLA08-NB02.ipynb.zip |
10.20KB |
| 01_boosting-a-decision-stump_instructions.html |
103.36KB |
| 01_boosting-a-decision-stump_lending-club-data.csv.zip |
18.53MB |
| 01_boosting-a-decision-stump_lending-club-data.gl.zip |
19.38MB |
| 01_boosting-a-decision-stump_lending-club-data.sframe.zip |
19.38MB |
| 01_boosting-a-decision-stump_module-8-assignment-2-test-idx.json.zip |
18.92KB |
| 01_boosting-a-decision-stump_module-8-assignment-2-train-idx.json.zip |
55.12KB |
| 01_boosting-a-decision-stump_module-8-boosting-assignment-2-blank.ipynb.zip |
9.76KB |
| 01_can-we-use-regularization-for-feature-selection.en.srt |
5.14KB |
| 01_can-we-use-regularization-for-feature-selection.en.txt |
3.32KB |
| 01_can-we-use-regularization-for-feature-selection.mp4 |
11.56MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search CLU02-NB01 ipynb zip |
7.61KB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search graphlab SFrame join html |
281.42KB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search instructions html |
123.56KB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki map index to word gl zip |
3.70MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki map index to word json zip |
5.04MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki tf idf npz zip |
50.92MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki word count npz zip |
23.19MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki csv zip |
39.86MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki gl zip |
55.57MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search people wiki sframe zip |
56.24MB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search sklearn feature extraction text CountVectorizer html |
57.54KB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search sklearn feature extraction text TfidfVectorizer html |
61.60KB |
| 01 choosing-features-and-metrics-for-nearest-neighbor-search sklearn metrics pairwise euclidean distances html |
21.74KB |
| 01_choosing-features-and-metrics-for-nearest-neighbor-search_stdtypes.html |
297.93KB |
| 01_choosing-step-size.en.srt |
7.57KB |
| 01_choosing-step-size.en.txt |
4.69KB |
| 01_choosing-step-size.mp4 |
14.40MB |
| 01 choosing-the-penalty-strength-and-other-practical-issues-with-lasso en srt |
7.18KB |
| 01 choosing-the-penalty-strength-and-other-practical-issues-with-lasso en txt |
4.67KB |
| 01_choosing-the-penalty-strength-and-other-practical-issues-with-lasso.mp4 |
19.38MB |
| 01_classification-ml-block-diagram.en.srt |
4.05KB |
| 01_classification-ml-block-diagram.en.txt |
2.50KB |
| 01_classification-ml-block-diagram.mp4 |
10.66MB |
| 01_clustering-and-similarity-ml-block-diagram.en.srt |
8.68KB |
| 01_clustering-and-similarity-ml-block-diagram.en.txt |
5.33KB |
| 01_clustering-and-similarity-ml-block-diagram.mp4 |
18.91MB |
| 01_clustering-documents-task-overview.en.srt |
3.39KB |
| 01_clustering-documents-task-overview.en.txt |
2.14KB |
| 01_clustering-documents-task-overview.mp4 |
9.84MB |
| 01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.gl.zip |
623.64KB |
| 01_clustering-text-data-with-gaussian-mixtures_4_map_index_to_word.json.zip |
834.29KB |
| 01_clustering-text-data-with-gaussian-mixtures_4_tf_idf.npz.zip |
3.24MB |
| 01_clustering-text-data-with-gaussian-mixtures_CLU04-NB02.ipynb.zip |
5.51KB |
| 01_clustering-text-data-with-gaussian-mixtures_em_utilities.py.zip |
2.79KB |
| 01_clustering-text-data-with-gaussian-mixtures_instructions.html |
21.26KB |
| 01_clustering-text-data-with-gaussian-mixtures_people_wiki.csv.zip |
39.86MB |
| 01_clustering-text-data-with-gaussian-mixtures_people_wiki.gl.zip |
55.57MB |
| 01_clustering-text-data-with-gaussian-mixtures_people_wiki.sframe.zip |
56.24MB |
| 01 clustering-text-data-with-gaussian-mixtures sklearn feature extraction text TfidfVectorizer html |
61.60KB |
| 01_clustering-text-data-with-k-means_CLU03-NB01.ipynb.zip |
13.96KB |
| 01_clustering-text-data-with-k-means_instructions.html |
78.83KB |
| 01_clustering-text-data-with-k-means_kmeans-arrays.npz.zip |
47.62MB |
| 01_clustering-text-data-with-k-means_numpy.argmin.html |
9.29KB |
| 01_clustering-text-data-with-k-means_numpy.mean.html |
12.24KB |
| 01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.gl.zip |
3.70MB |
| 01_clustering-text-data-with-k-means_people_wiki_map_index_to_word.json.zip |
5.04MB |
| 01_clustering-text-data-with-k-means_people_wiki_tf_idf.npz.zip |
50.92MB |
| 01_clustering-text-data-with-k-means_people_wiki.csv.zip |
39.86MB |
| 01_clustering-text-data-with-k-means_people_wiki.gl.zip |
55.57MB |
| 01_clustering-text-data-with-k-means_people_wiki.sframe.zip |
56.24MB |
| 01_clustering-text-data-with-k-means_pyplot_api.html |
330B |
| 01_clustering-text-data-with-k-means_sklearn.cluster.KMeans.html |
60.77KB |
| 01 clustering-text-data-with-k-means sklearn feature extraction text TfidfVectorizer html |
61.60KB |
| 01_clustering-text-data-with-k-means_sklearn.preprocessing.normalize.html |
18.16KB |
| 01_collaborative-filtering-people-who-bought-this-also-bought.en.srt |
6.79KB |
| 01_collaborative-filtering-people-who-bought-this-also-bought.en.txt |
4.17KB |
| 01_collaborative-filtering-people-who-bought-this-also-bought.mp4 |
15.26MB |
| 01_collaborative-filtering-vs-content-based-filtering.en.srt |
14.32KB |
| 01_collaborative-filtering-vs-content-based-filtering.en.txt |
7.42KB |
| 01_collaborative-filtering-vs-content-based-filtering.mp4 |
19.97MB |
| 01_complexity-of-brute-force-search.en.srt |
2.67KB |
| 01_complexity-of-brute-force-search.en.txt |
1.61KB |
| 01_complexity-of-brute-force-search.mp4 |
5.92MB |
| 01_computing-soft-assignments-from-known-cluster-parameters.en.srt |
9.41KB |
| 01_computing-soft-assignments-from-known-cluster-parameters.en.txt |
5.96KB |
| 01_computing-soft-assignments-from-known-cluster-parameters.mp4 |
21.76MB |
| 01_computing-the-gradient-of-rss.en.srt |
3.71KB |
| 01_computing-the-gradient-of-rss.en.srt |
8.31KB |
| 01_computing-the-gradient-of-rss.en.txt |
4.93KB |
| 01_computing-the-gradient-of-rss.en.txt |
2.23KB |
| 01_computing-the-gradient-of-rss.mp4 |
17.03MB |
| 01_computing-the-gradient-of-rss.mp4 |
7.25MB |
| 01_computing-the-gradient-of-the-ridge-objective.en.srt |
5.73KB |
| 01_computing-the-gradient-of-the-ridge-objective.en.txt |
3.58KB |
| 01_computing-the-gradient-of-the-ridge-objective.mp4 |
14.78MB |
| 01_coordinate-descent-for-lasso-normalized-features.en.srt |
5.69KB |
| 01_coordinate-descent-for-lasso-normalized-features.en.txt |
3.54KB |
| 01_coordinate-descent-for-lasso-normalized-features.mp4 |
12.61MB |
| 01_cost-function-for-logistic-regression.en.srt |
17.10KB |
| 01_cost-function-for-logistic-regression.en.txt |
8.90KB |
| 01_cost-function-for-logistic-regression.mp4 |
24.61MB |
| 01_course-overview.en.srt |
4.86KB |
| 01_course-overview.en.txt |
3.04KB |
| 01_course-overview.mp4 |
11.43MB |
| 01_deciding-what-to-try-next.en.srt |
6.77KB |
| 01_deciding-what-to-try-next.en.txt |
3.56KB |
| 01_deciding-what-to-try-next.mp4 |
11.45MB |
| 01_decision-tree-model.en.srt |
10.84KB |
| 01_decision-tree-model.en.txt |
5.65KB |
| 01_decision-tree-model.mp4 |
14.76MB |
| 01_decision-trees-in-practice_CLA06-NB01.ipynb.zip |
8.64KB |
| 01_decision-trees-in-practice_instructions.html |
38.68KB |
| 01_decision-trees-in-practice_lending-club-data.csv.zip |
18.53MB |
| 01_decision-trees-in-practice_lending-club-data.gl.zip |
19.38MB |
| 01_decision-trees-in-practice_lending-club-data.sframe.zip |
19.38MB |
| 01_decision-trees-in-practice_module-6-assignment-train-idx.json.zip |
55.12KB |
| 01_decision-trees-in-practice_module-6-assignment-validation-idx.json.zip |
18.93KB |
| 01 decision-trees-in-practice module-6-decision-tree-practical-assignment-blank zip |
8.22KB |
| 01_deep-features-for-image-retrieval-assignment_image_test_data.csv |
103.00MB |
| 01_deep-features-for-image-retrieval-assignment_image_test_data.zip |
50.92MB |
| 01_deep-features-for-image-retrieval-assignment_image_train_data.csv |
51.58MB |
| 01_deep-features-for-image-retrieval-assignment_image_train_data.zip |
25.35MB |
| 01_deep-features-for-image-retrieval-assignment_instructions.html |
350.39KB |
| 01_deep-features-for-image-retrieval-assignment_nearest_neighbors.html |
243.25KB |
| 01_deep-features-for-image-retrieval-assignment_nearest_neighbors.md |
176.11KB |
| 01_deep-features-for-image-retrieval-assignment_tabular-data.html |
243.25KB |
| 01_deep-features-for-image-retrieval-assignment_turicreate.Sketch.html |
28.19KB |
| 01_deep-learning-ml-block-diagram.en.srt |
4.00KB |
| 01_deep-learning-ml-block-diagram.en.txt |
2.45KB |
| 01_deep-learning-ml-block-diagram.mp4 |
11.14MB |
| 01_defining-our-least-squares-optimization-objective.en.srt |
3.85KB |
| 01_defining-our-least-squares-optimization-objective.en.txt |
2.46KB |
| 01_defining-our-least-squares-optimization-objective.mp4 |
10.19MB |
| 01_deriving-the-lasso-coordinate-descent-update.en.srt |
20.22KB |
| 01_deriving-the-lasso-coordinate-descent-update.en.txt |
12.15KB |
| 01_deriving-the-lasso-coordinate-descent-update.mp4 |
42.70MB |
| 01_diagnosing-bias-and-variance.en.srt |
17.79KB |
| 01_diagnosing-bias-and-variance.en.txt |
9.25KB |
| 01_diagnosing-bias-and-variance.mp4 |
20.30MB |
| 01_document-representation.en.srt |
7.49KB |
| 01_document-representation.en.txt |
4.68KB |
| 01_document-representation.mp4 |
14.08MB |
| 01_download-notebooks-to-follow-along_instructions.html |
1.79KB |
| 01_download-notebooks-to-follow-along_Philadelphia_Crime_Rate_noNA.csv.zip |
2.12KB |
| 01_download-notebooks-to-follow-along_PhillyCrime.ipynb.zip |
44.94KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along amazon baby sframe zip |
40.33MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND02-NB01 ipynb zip |
67.26KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND03-NB01 ipynb zip |
13.08KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND04-NB01 ipynb zip |
11.61KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND05-NB01 ipynb zip |
15.14KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND06-NB01 ipynb zip |
4.98KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along FND06-NB02 ipynb zip |
1.91KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along Getting started with Jupyter Notebook ipynb zip |
1.98KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along home data sframe zip |
908.17KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along house images zip |
4.66MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along image test data zip |
50.92MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along image test data zip |
50.92MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along image train data zip |
25.35MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along image train data zip |
25.35MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
1.63KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
2.26KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
2.54KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
1.98KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
1.98KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
1.98KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
2.23KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along instructions html |
2.42KB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along people wiki sframe zip |
56.24MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along people-example csv |
248B |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along song data sframe zip |
47.97MB |
| 01 download-the-jupyter-notebook-used-in-this-lesson-to-follow-along Turi Getting Started with SFrames ipynb zip |
1.73KB |
| 01_download-wiki-people-data_instructions.html |
1.13KB |
| 01_download-wiki-people-data_people_wiki.sframe.zip |
56.24MB |
| 01_em-iterates-in-equations-and-pictures.en.srt |
7.61KB |
| 01_em-iterates-in-equations-and-pictures.en.txt |
4.80KB |
| 01_em-iterates-in-equations-and-pictures.mp4 |
16.69MB |
| 01_encoding-categorical-inputs.en.srt |
6.10KB |
| 01_encoding-categorical-inputs.en.txt |
3.82KB |
| 01_encoding-categorical-inputs.mp4 |
14.56MB |
| 01_ensemble-methods-impact-of-boosting-quick-recap.en.srt |
6.70KB |
| 01_ensemble-methods-impact-of-boosting-quick-recap.en.txt |
4.18KB |
| 01_ensemble-methods-impact-of-boosting-quick-recap.mp4 |
16.20MB |
| 01_error-metrics-for-skewed-datasets.en.srt |
17.02KB |
| 01_error-metrics-for-skewed-datasets.en.txt |
8.89KB |
| 01_error-metrics-for-skewed-datasets.mp4 |
18.95MB |
| 01_evaluating-overfitting-via-training-test-split.en.srt |
8.64KB |
| 01_evaluating-overfitting-via-training-test-split.en.txt |
5.46KB |
| 01_evaluating-overfitting-via-training-test-split.mp4 |
15.44MB |
| 01_example-of-adaboost-in-action.en.srt |
6.79KB |
| 01_example-of-adaboost-in-action.en.txt |
4.27KB |
| 01_example-of-adaboost-in-action.mp4 |
11.87MB |
| 01_example-of-continuous-state-space-applications.en.srt |
9.68KB |
| 01_example-of-continuous-state-space-applications.en.txt |
5.03KB |
| 01_example-of-continuous-state-space-applications.mp4 |
27.05MB |
| 01 exploring-different-multiple-regression-models-for-house-price-prediction home data sframe zip |
908.17KB |
| 01 exploring-different-multiple-regression-models-for-house-price-prediction instructions html |
10.02KB |
| 01 exploring-different-multiple-regression-models-for-house-price-prediction kc house data csv zip |
780.66KB |
| 01 exploring-different-multiple-regression-models-for-house-price-prediction kc house test data csv zip |
154.80KB |
| 01 exploring-different-multiple-regression-models-for-house-price-prediction kc house train data csv zip |
628.45KB |
| 01 exploring-different-multiple-regression-models-for-house-price-prediction REG02-NB01 ipynb zip |
3.44KB |
| 01_exploring-ensemble-methods_boosted_trees_classifier.html |
283.64KB |
| 01_exploring-ensemble-methods_CLA08-NB01.ipynb.zip |
7.11KB |
| 01_exploring-ensemble-methods_graphlab.SFrame.to_numpy.html |
283.64KB |
| 01_exploring-ensemble-methods_instructions.html |
31.24KB |
| 01_exploring-ensemble-methods_lending-club-data.csv.zip |
18.53MB |
| 01_exploring-ensemble-methods_lending-club-data.gl.zip |
19.38MB |
| 01_exploring-ensemble-methods_lending-club-data.sframe.zip |
19.38MB |
| 01_exploring-ensemble-methods_module-8-assignment-1-train-idx.json.zip |
55.03KB |
| 01_exploring-ensemble-methods_module-8-assignment-1-validation-idx.json.zip |
18.93KB |
| 01 exploring-ensemble-methods sklearn ensemble GradientBoostingClassifier html |
83.40KB |
| 01_exploring-precision-and-recall_amazon_baby.csv.zip |
28.67MB |
| 01_exploring-precision-and-recall_amazon_baby.gl.zip |
40.34MB |
| 01_exploring-precision-and-recall_amazon_baby.sframe.zip |
40.33MB |
| 01_exploring-precision-and-recall_CLA09-NB01.ipynb.zip |
6.34KB |
| 01_exploring-precision-and-recall_instructions.html |
70.88KB |
| 01_exploring-precision-and-recall_module-9-assignment-test-idx.json.zip |
46.63KB |
| 01_exploring-precision-and-recall_module-9-assignment-train-idx.json.zip |
163.92KB |
| 01_feature-scaling-part-1.en.srt |
7.56KB |
| 01_feature-scaling-part-1.en.txt |
4.78KB |
| 01_feature-scaling-part-1.mp4 |
13.64MB |
| 01_finding-unusual-events.en.srt |
14.54KB |
| 01_finding-unusual-events.en.txt |
9.41KB |
| 01_finding-unusual-events.mp4 |
26.28MB |
| 01 fitting-a-simple-linear-regression-model-on-housing-data home data sframe zip |
908.17KB |
| 01 fitting-a-simple-linear-regression-model-on-housing-data instructions html |
9.46KB |
| 01 fitting-a-simple-linear-regression-model-on-housing-data kc house data csv zip |
780.66KB |
| 01 fitting-a-simple-linear-regression-model-on-housing-data kc house test data csv zip |
154.80KB |
| 01 fitting-a-simple-linear-regression-model-on-housing-data kc house train data csv zip |
628.45KB |
| 01 fitting-a-simple-linear-regression-model-on-housing-data REG01-NB01 ipynb zip |
4.07KB |
| 01_formally-defining-the-3-sources-of-error.en.srt |
19.32KB |
| 01_formally-defining-the-3-sources-of-error.en.txt |
12.15KB |
| 01_formally-defining-the-3-sources-of-error.mp4 |
45.24MB |
| 01_forward-prop-in-a-single-layer.en.srt |
6.05KB |
| 01_forward-prop-in-a-single-layer.en.txt |
3.84KB |
| 01_forward-prop-in-a-single-layer.mp4 |
12.38MB |
| 01_from-weighted-k-nn-to-kernel-regression.en.srt |
8.91KB |
| 01_from-weighted-k-nn-to-kernel-regression.en.txt |
5.63KB |
| 01_from-weighted-k-nn-to-kernel-regression.mp4 |
18.56MB |
| 01 getting-started-with-python-jupyter-notebook-turi-create Getting started with Jupyter Notebook ipynb zip |
1.98KB |
| 01 getting-started-with-python-jupyter-notebook-turi-create instructions html |
13.19KB |
| 01 getting-started-with-python-jupyter-notebook-turi-create people-example csv |
248B |
| 01 getting-started-with-python-jupyter-notebook-turi-create Turi Getting Started with SFrames ipynb zip |
1.73KB |
| 01_gradient-descent.en.srt |
12.24KB |
| 01_gradient-descent.en.txt |
6.40KB |
| 01_gradient-descent.mp4 |
22.48MB |
| 01_gradient-descent-implementation.en.srt |
9.61KB |
| 01_gradient-descent-implementation.en.txt |
5.03KB |
| 01_gradient-descent-implementation.mp4 |
12.76MB |
| 01_gradients-derivatives-in-multiple-dimensions.en.srt |
6.32KB |
| 01_gradients-derivatives-in-multiple-dimensions.en.txt |
3.70KB |
| 01_gradients-derivatives-in-multiple-dimensions.mp4 |
14.01MB |
| 01_how-neural-networks-are-implemented-efficiently.en.srt |
6.12KB |
| 01_how-neural-networks-are-implemented-efficiently.en.txt |
3.17KB |
| 01_how-neural-networks-are-implemented-efficiently.mp4 |
12.24MB |
| 01_how-we-got-into-ml.en.srt |
5.53KB |
| 01_how-we-got-into-ml.en.txt |
3.35KB |
| 01_how-we-got-into-ml.mp4 |
19.12MB |
| 01_identifying-safe-loans-with-decision-trees_CLA05-NB01.ipynb.zip |
6.21KB |
| 01_identifying-safe-loans-with-decision-trees_graphlab.SFrame.to_numpy.html |
281.42KB |
| 01_identifying-safe-loans-with-decision-trees_instructions.html |
25.01KB |
| 01_identifying-safe-loans-with-decision-trees_lending-club-data.csv.zip |
18.53MB |
| 01_identifying-safe-loans-with-decision-trees_lending-club-data.gl.zip |
19.38MB |
| 01_identifying-safe-loans-with-decision-trees_lending-club-data.sframe.zip |
19.38MB |
| 01 identifying-safe-loans-with-decision-trees module-5-assignment-1-train-idx json zip |
55.12KB |
| 01 identifying-safe-loans-with-decision-trees module-5-assignment-1-validation-idx json zip |
18.94KB |
| 01 identifying-safe-loans-with-decision-trees sklearn tree DecisionTreeClassifier html |
74.90KB |
| 01_implementing-binary-decision-trees_CLA05-NB02.ipynb.zip |
8.24KB |
| 01_implementing-binary-decision-trees_instructions.html |
30.75KB |
| 01_implementing-binary-decision-trees_lending-club-data.csv.zip |
18.53MB |
| 01_implementing-binary-decision-trees_lending-club-data.gl.zip |
19.38MB |
| 01_implementing-binary-decision-trees_lending-club-data.sframe.zip |
19.38MB |
| 01 implementing-binary-decision-trees module-5-assignment-2-test-idx json zip |
18.92KB |
| 01 implementing-binary-decision-trees module-5-assignment-2-train-idx json zip |
55.12KB |
| 01 implementing-binary-decision-trees module-5-decision-tree-assignment-2-blank ipynb zip |
7.74KB |
| 01_implementing-em-for-gaussian-mixtures_chosen_images.png |
352.89KB |
| 01_implementing-em-for-gaussian-mixtures_CLU04-NB01.ipynb.zip |
11.17KB |
| 01_implementing-em-for-gaussian-mixtures_images.sf.zip |
11.18MB |
| 01_implementing-em-for-gaussian-mixtures_images.zip |
13.79MB |
| 01_implementing-em-for-gaussian-mixtures_instructions.html |
786.63KB |
| 01_implementing-em-for-gaussian-mixtures_LinearAlgebraReview.html |
131.87KB |
| 01 implementing-em-for-gaussian-mixtures scipy stats multivariate normal html |
12.87KB |
| 01_implementing-lasso-using-coordinate-descent_home_data.sframe.zip |
908.17KB |
| 01_implementing-lasso-using-coordinate-descent_instructions.html |
19.62KB |
| 01_implementing-lasso-using-coordinate-descent_kc_house_data.csv.zip |
780.66KB |
| 01_implementing-lasso-using-coordinate-descent_kc_house_test_data.csv.zip |
154.80KB |
| 01_implementing-lasso-using-coordinate-descent_kc_house_train_data.csv.zip |
628.45KB |
| 01_implementing-lasso-using-coordinate-descent_numpy-tutorial-py3.ipynb.zip |
2.92KB |
| 01_implementing-lasso-using-coordinate-descent_REG05-NB02.ipynb.zip |
5.66KB |
| 01 implementing-locality-sensitive-hashing-from-scratch CLU02-NB02 ipynb zip |
10.09KB |
| 01_implementing-locality-sensitive-hashing-from-scratch_instructions.html |
328.03KB |
| 01_implementing-locality-sensitive-hashing-from-scratch_itertools.html |
161.25KB |
| 01 implementing-locality-sensitive-hashing-from-scratch people wiki map index to word gl zip |
3.70MB |
| 01 implementing-locality-sensitive-hashing-from-scratch people wiki map index to word json zip |
5.04MB |
| 01 implementing-locality-sensitive-hashing-from-scratch people wiki tf idf npz zip |
50.92MB |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.csv.zip |
39.86MB |
| 01_implementing-locality-sensitive-hashing-from-scratch_people_wiki.gl.zip |
55.57MB |
| 01 implementing-locality-sensitive-hashing-from-scratch people wiki sframe zip |
56.24MB |
| 01 implementing-locality-sensitive-hashing-from-scratch sklearn feature extraction text TfidfVectorizer html |
61.60KB |
| 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.csv.zip |
9.16MB |
| 01_implementing-logistic-regression-from-scratch_amazon_baby_subset.gl.zip |
12.73MB |
| 01 implementing-logistic-regression-from-scratch amazon baby subset sframe zip |
12.73MB |
| 01_implementing-logistic-regression-from-scratch_CLA03-NB01.ipynb.zip |
6.92KB |
| 01_implementing-logistic-regression-from-scratch_important_words.json.zip |
862B |
| 01_implementing-logistic-regression-from-scratch_instructions.html |
30.78KB |
| 01 implementing-logistic-regression-from-scratch module-3-assignment-numpy-arrays npz zip |
1.16MB |
| 01 implementing-logistic-regression-from-scratch module-3-linear-classifier-learning-assignment-blank ipynb zip |
6.41KB |
| 01_implementing-ridge-regression-via-gradient-descent_home_data.sframe.zip |
908.17KB |
| 01_implementing-ridge-regression-via-gradient-descent_instructions.html |
18.59KB |
| 01_implementing-ridge-regression-via-gradient-descent_kc_house_data.csv.zip |
780.66KB |
| 01 implementing-ridge-regression-via-gradient-descent kc house test data csv zip |
154.80KB |
| 01 implementing-ridge-regression-via-gradient-descent kc house train data csv zip |
628.45KB |
| 01 implementing-ridge-regression-via-gradient-descent numpy-tutorial-py3 ipynb zip |
2.92KB |
| 01_implementing-ridge-regression-via-gradient-descent_REG04-NB02.ipynb.zip |
4.71KB |
| 01 important-update-regarding-the-machine-learning-specialization instructions html |
1.75KB |
| 01 important-update-regarding-the-machine-learning-specialization instructions html |
1.75KB |
| 01 important-update-regarding-the-machine-learning-specialization instructions html |
1.75KB |
| 01 important-update-regarding-the-machine-learning-specialization instructions html |
1.75KB |
| 01_inference-in-code.en.srt |
10.03KB |
| 01_inference-in-code.en.txt |
5.25KB |
| 01_inference-in-code.mp4 |
16.82MB |
| 01_irreducible-error-and-bias.en.srt |
8.80KB |
| 01_irreducible-error-and-bias.en.txt |
5.49KB |
| 01_irreducible-error-and-bias.mp4 |
20.89MB |
| 01_is-there-a-path-to-agi.en.srt |
16.42KB |
| 01_is-there-a-path-to-agi.en.txt |
8.59KB |
| 01_is-there-a-path-to-agi.mp4 |
28.09MB |
| 01_iterative-loop-of-ml-development.en.srt |
12.14KB |
| 01_iterative-loop-of-ml-development.en.txt |
6.44KB |
| 01_iterative-loop-of-ml-development.mp4 |
14.81MB |
| 01_k-nearest-neighbors-regression.en.srt |
9.30KB |
| 01_k-nearest-neighbors-regression.en.txt |
5.85KB |
| 01_k-nearest-neighbors-regression.mp4 |
19.30MB |
| 01_limitations-of-kd-trees.en.srt |
5.22KB |
| 01_limitations-of-kd-trees.en.txt |
3.31KB |
| 01_limitations-of-kd-trees.mp4 |
11.58MB |
| 01_linear-regression-model-part-1.en.srt |
14.76KB |
| 01_linear-regression-model-part-1.en.txt |
7.58KB |
| 01_linear-regression-model-part-1.mp4 |
20.27MB |
| 01_logistic-regression-with-l2-regularization_amazon_baby_subset.csv.zip |
9.16MB |
| 01_logistic-regression-with-l2-regularization_amazon_baby_subset.gl.zip |
12.73MB |
| 01_logistic-regression-with-l2-regularization_amazon_baby_subset.sframe.zip |
12.73MB |
| 01_logistic-regression-with-l2-regularization_CLA04-NB01.ipynb.zip |
7.46KB |
| 01_logistic-regression-with-l2-regularization_important_words.json.zip |
862B |
| 01_logistic-regression-with-l2-regularization_instructions.html |
90.47KB |
| 01 logistic-regression-with-l2-regularization module-4-assignment-numpy-arrays npz zip |
1.16MB |
| 01 logistic-regression-with-l2-regularization module-4-assignment-train-idx json zip |
47.14KB |
| 01 logistic-regression-with-l2-regularization module-4-assignment-validation-idx json zip |
16.93KB |
| 01 logistic-regression-with-l2-regularization module-4-linear-classifier-regularization-assignment-blank ipynb zip |
7.03KB |
| 01_making-predictions-with-decision-trees.en.srt |
1.82KB |
| 01_making-predictions-with-decision-trees.en.txt |
1.10KB |
| 01_making-predictions-with-decision-trees.mp4 |
4.84MB |
| 01_making-recommendations.en.srt |
7.20KB |
| 01_making-recommendations.en.txt |
4.59KB |
| 01_making-recommendations.mp4 |
20.44MB |
| 01_mean-normalization.en.srt |
10.72KB |
| 01_mean-normalization.en.txt |
6.73KB |
| 01_mean-normalization.mp4 |
18.90MB |
| 01_measuring-purity.en.srt |
10.17KB |
| 01_measuring-purity.en.txt |
5.39KB |
| 01_measuring-purity.mp4 |
15.97MB |
| 01_mixture-of-gaussians.en.srt |
8.76KB |
| 01_mixture-of-gaussians.en.txt |
5.58KB |
| 01_mixture-of-gaussians.mp4 |
21.04MB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_CLU06-NB01.ipynb.zip |
5.47KB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_em_utilities.py.zip |
2.79KB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_instructions.html |
20.54KB |
| 01 modeling-text-data-with-a-hierarchy-of-clusters people wiki map index to word gl zip |
3.70MB |
| 01 modeling-text-data-with-a-hierarchy-of-clusters people wiki map index to word json zip |
5.04MB |
| 01 modeling-text-data-with-a-hierarchy-of-clusters people wiki tf idf npz zip |
50.92MB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.csv.zip |
39.86MB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.gl.zip |
55.57MB |
| 01_modeling-text-data-with-a-hierarchy-of-clusters_people_wiki.sframe.zip |
56.24MB |
| 01 modeling-text-data-with-a-hierarchy-of-clusters sklearn feature extraction text TfidfVectorizer html |
61.60KB |
| 01 modeling-text-topics-with-latent-dirichlet-allocation CLU05-NB01 ipynb zip |
8.95KB |
| 01_modeling-text-topics-with-latent-dirichlet-allocation_instructions.html |
5.53KB |
| 01 modeling-text-topics-with-latent-dirichlet-allocation people wiki sframe zip |
56.24MB |
| 01_modeling-text-topics-with-latent-dirichlet-allocation_topic_models.zip |
87.67MB |
| 01_modifying-decision-trees-to-handle-missing-data.en.srt |
6.43KB |
| 01_modifying-decision-trees-to-handle-missing-data.en.txt |
4.02KB |
| 01_modifying-decision-trees-to-handle-missing-data.mp4 |
14.41MB |
| 01_motivating-mapreduce.en.srt |
10.28KB |
| 01_motivating-mapreduce.en.txt |
6.28KB |
| 01_motivating-mapreduce.mp4 |
20.39MB |
| 01_motivating-the-use-of-multiple-inputs.en.srt |
6.00KB |
| 01_motivating-the-use-of-multiple-inputs.en.txt |
3.92KB |
| 01_motivating-the-use-of-multiple-inputs.mp4 |
14.17MB |
| 01_motivations.en.srt |
12.69KB |
| 01_motivations.en.txt |
7.84KB |
| 01_motivations.mp4 |
20.96MB |
| 01_multiclass.en.srt |
4.28KB |
| 01_multiclass.en.txt |
2.76KB |
| 01_multiclass.mp4 |
8.37MB |
| 01_multiple-features.en.srt |
13.43KB |
| 01_multiple-features.en.txt |
6.94KB |
| 01_multiple-features.mp4 |
18.89MB |
| 01_neural-network-layer.en.srt |
12.99KB |
| 01_neural-network-layer.en.txt |
6.84KB |
| 01_neural-network-layer.mp4 |
20.43MB |
| 01_numpy-tutorial_instructions.html |
2.22KB |
| 01_numpy-tutorial_numpy-tutorial.ipynb.zip |
2.95KB |
| 01_numpy-tutorial_quickstart.html |
162.86KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression home data sframe zip |
908.17KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression instructions html |
19.77KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression kc house data csv zip |
780.66KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression numpy-tutorial-py3 ipynb zip |
2.92KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression REG04-NB01 ipynb zip |
5.90KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 1 data csv zip |
158.64KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 2 data csv zip |
157.64KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 3 data csv zip |
158.66KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house set 4 data csv zip |
158.30KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house test data csv zip |
81.70KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house train data csv zip |
354.68KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house train valid shuffled csv zip |
609.97KB |
| 01 observing-effects-of-l2-penalty-in-polynomial-regression wk3 kc house valid data csv zip |
349.96KB |
| 01_open-challenges-in-ml.en.srt |
12.05KB |
| 01_open-challenges-in-ml.en.txt |
7.61KB |
| 01_open-challenges-in-ml.mp4 |
33.33MB |
| 01_optional-motivating-pruning.en.srt |
10.36KB |
| 01_optional-motivating-pruning.en.txt |
6.36KB |
| 01_optional-motivating-pruning.mp4 |
21.63MB |
| 01_optional-reading-review-of-matrix-algebra_instructions.html |
1.12KB |
| 01_other-applications-of-clustering.en.srt |
9.68KB |
| 01_other-applications-of-clustering.en.txt |
6.13KB |
| 01_other-applications-of-clustering.mp4 |
21.55MB |
| 01_overfitting-in-classifiers-leads-to-overconfident-predictions.en.srt |
6.55KB |
| 01_overfitting-in-classifiers-leads-to-overconfident-predictions.en.txt |
4.04KB |
| 01_overfitting-in-classifiers-leads-to-overconfident-predictions.mp4 |
14.13MB |
| 01_penalizing-large-coefficients-to-mitigate-overfitting.en.srt |
7.01KB |
| 01_penalizing-large-coefficients-to-mitigate-overfitting.en.txt |
4.36KB |
| 01_penalizing-large-coefficients-to-mitigate-overfitting.mp4 |
14.82MB |
| 01_performance-of-nn-as-amount-of-data-grows.en.srt |
10.00KB |
| 01_performance-of-nn-as-amount-of-data-grows.en.txt |
6.32KB |
| 01_performance-of-nn-as-amount-of-data-grows.mp4 |
21.87MB |
| 01_polynomial-regression_home_data.sframe.zip |
908.17KB |
| 01_polynomial-regression_instructions.html |
14.47KB |
| 01_polynomial-regression_kc_house_data.csv.zip |
780.66KB |
| 01_polynomial-regression_numpy-tutorial-py3.ipynb.zip |
2.92KB |
| 01_polynomial-regression_REG03-NB01.ipynb.zip |
4.20KB |
| 01_polynomial-regression_wk3_kc_house_set_1_data.csv.zip |
158.64KB |
| 01_polynomial-regression_wk3_kc_house_set_2_data.csv.zip |
157.64KB |
| 01_polynomial-regression_wk3_kc_house_set_3_data.csv.zip |
158.66KB |
| 01_polynomial-regression_wk3_kc_house_set_4_data.csv.zip |
158.30KB |
| 01_polynomial-regression_wk3_kc_house_test_data.csv.zip |
81.70KB |
| 01_polynomial-regression_wk3_kc_house_train_data.csv.zip |
354.68KB |
| 01_polynomial-regression_wk3_kc_house_valid_data.csv.zip |
349.96KB |
| 01_practice-quiz-classification-with-logistic-regression_exam.html |
51.46KB |
| 01_practice-quiz-cost-function-for-logistic-regression_exam.html |
77.00KB |
| 01_practice-quiz-gradient-descent-for-logistic-regression_exam.html |
102.06KB |
| 01_practice-quiz-gradient-descent-in-practice_exam.html |
164.85KB |
| 01_practice-quiz-multiple-linear-regression_exam.html |
69.77KB |
| 01_practice-quiz-regression_exam.html |
2.63KB |
| 01_practice-quiz-supervised-vs-unsupervised-learning_exam.html |
1.94KB |
| 01_practice-quiz-the-problem-of-overfitting_exam.html |
89.75KB |
| 01_practice-quiz-train-the-model-with-gradient-descent_exam.html |
21.37KB |
| 01 precision-fraction-of-positive-predictions-that-are-actually-positive en srt |
7.54KB |
| 01 precision-fraction-of-positive-predictions-that-are-actually-positive en txt |
4.56KB |
| 01 precision-fraction-of-positive-predictions-that-are-actually-positive mp4 |
15.57MB |
| 01_precision-recall-extremes.en.srt |
3.65KB |
| 01_precision-recall-extremes.en.txt |
2.23KB |
| 01_precision-recall-extremes.mp4 |
8.63MB |
| 01_predicting-class-probabilities.en.srt |
2.29KB |
| 01_predicting-class-probabilities.en.txt |
1.40KB |
| 01_predicting-class-probabilities.mp4 |
5.70MB |
| 01_predicting-class-probabilities-with-generalized-linear-models.en.srt |
6.88KB |
| 01_predicting-class-probabilities-with-generalized-linear-models.en.txt |
4.30KB |
| 01_predicting-class-probabilities-with-generalized-linear-models.mp4 |
19.87MB |
| 01_predicting-house-prices-assignment_FND02-NB01.ipynb.zip |
67.26KB |
| 01_predicting-house-prices-assignment_graphlab.SFrame.html |
281.42KB |
| 01_predicting-house-prices-assignment_home_data.csv |
2.48MB |
| 01_predicting-house-prices-assignment_home_data.sframe.zip |
908.17KB |
| 01_predicting-house-prices-assignment_house_images.zip |
4.66MB |
| 01_predicting-house-prices-assignment_instructions.html |
29.36KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression home data small sframe zip |
386.86KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression instructions html |
19.95KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression kc house data small test csv zip |
55.18KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression kc house data small train csv zip |
171.65KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression kc house data small validation csv zip |
45.71KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression kc house data small csv zip |
269.25KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression numpy argsort html |
38.84KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression numpy ndarray shape html |
7.20KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression numpy-tutorial-py3 ipynb zip |
2.92KB |
| 01 predicting-house-prices-using-k-nearest-neighbors-regression REG06-NB01 ipynb zip |
5.29KB |
| 01_predicting-sentiment-from-product-reviews_amazon_baby.csv.zip |
28.67MB |
| 01_predicting-sentiment-from-product-reviews_amazon_baby.gl.zip |
40.34MB |
| 01_predicting-sentiment-from-product-reviews_amazon_baby.sframe.zip |
40.33MB |
| 01_predicting-sentiment-from-product-reviews_CLA02-NB01.ipynb.zip |
6.46KB |
| 01_predicting-sentiment-from-product-reviews_instructions.html |
24.88KB |
| 01 predicting-sentiment-from-product-reviews module-2-assignment-test-idx json zip |
46.63KB |
| 01 predicting-sentiment-from-product-reviews module-2-assignment-train-idx json zip |
163.92KB |
| 01 predicting-sentiment-from-product-reviews sklearn linear model LogisticRegression html |
82.61KB |
| 01_principle-of-occams-razor-learning-simpler-decision-trees.en.srt |
6.67KB |
| 01_principle-of-occams-razor-learning-simpler-decision-trees.en.txt |
4.19KB |
| 01_principle-of-occams-razor-learning-simpler-decision-trees.mp4 |
14.50MB |
| 01_recap-of-decision-trees.en.srt |
1.27KB |
| 01_recap-of-decision-trees.en.txt |
804B |
| 01_recap-of-decision-trees.mp4 |
3.43MB |
| 01_recap-of-handling-missing-data.en.srt |
2.21KB |
| 01_recap-of-handling-missing-data.en.txt |
1.39KB |
| 01_recap-of-handling-missing-data.mp4 |
6.18MB |
| 01_recap-of-learning-logistic-regression-classifiers.en.srt |
2.55KB |
| 01_recap-of-learning-logistic-regression-classifiers.en.txt |
1.60KB |
| 01_recap-of-learning-logistic-regression-classifiers.mp4 |
6.96MB |
| 01_recap-of-logistic-regression-classifier.en.srt |
1.89KB |
| 01_recap-of-logistic-regression-classifier.en.txt |
1.17KB |
| 01_recap-of-logistic-regression-classifier.mp4 |
5.20MB |
| 01_recap-of-overfitting-and-regularization-in-decision-trees.en.srt |
1.71KB |
| 01_recap-of-overfitting-and-regularization-in-decision-trees.en.txt |
1.02KB |
| 01_recap-of-overfitting-and-regularization-in-decision-trees.mp4 |
4.85MB |
| 01_recap-of-overfitting-regularization-in-logistic-regression.en.srt |
1.31KB |
| 01_recap-of-overfitting-regularization-in-logistic-regression.en.txt |
808B |
| 01_recap-of-overfitting-regularization-in-logistic-regression.mp4 |
3.83MB |
| 01_recap-of-precision-recall.en.srt |
2.06KB |
| 01_recap-of-precision-recall.en.txt |
1.31KB |
| 01_recap-of-precision-recall.mp4 |
5.11MB |
| 01_recommender-systems-ml-block-diagram.en.srt |
5.79KB |
| 01_recommender-systems-ml-block-diagram.en.txt |
3.61KB |
| 01_recommender-systems-ml-block-diagram.mp4 |
12.28MB |
| 01_recommending-songs-assignment_FND05-NB01.ipynb.zip |
15.14KB |
| 01_recommending-songs-assignment_graphlab.SFrame.groupby.html |
281.42KB |
| 01_recommending-songs-assignment_instructions.html |
10.68KB |
| 01_recommending-songs-assignment_song_data.csv |
149.16MB |
| 01_recommending-songs-assignment_song_data.sframe.zip |
47.97MB |
| 01_recursive-greedy-algorithm.en.srt |
5.78KB |
| 01_recursive-greedy-algorithm.en.txt |
3.67KB |
| 01_recursive-greedy-algorithm.mp4 |
11.15MB |
| 01_reducing-the-number-of-features-optional.en.srt |
18.06KB |
| 01_reducing-the-number-of-features-optional.en.txt |
9.38KB |
| 01_reducing-the-number-of-features-optional.mp4 |
26.70MB |
| 01_regression-ml-block-diagram.en.srt |
7.79KB |
| 01_regression-ml-block-diagram.en.txt |
4.92KB |
| 01_regression-ml-block-diagram.mp4 |
15.47MB |
| 01_retrieving-wikipedia-articles-assignment_FND04-NB01.ipynb.zip |
11.61KB |
| 01_retrieving-wikipedia-articles-assignment_instructions.html |
9.04KB |
| 01_retrieving-wikipedia-articles-assignment_people_wiki.csv |
110.85MB |
| 01_retrieving-wikipedia-articles-assignment_people_wiki.sframe.zip |
56.24MB |
| 01 retrieving-wikipedia-articles-assignment turicreate toolkits distances cosine html |
10.82KB |
| 01_review-of-gradient-ascent.en.srt |
7.70KB |
| 01_review-of-gradient-ascent.en.txt |
4.79KB |
| 01_review-of-gradient-ascent.mp4 |
15.46MB |
| 01_scaling-to-huge-datasets-through-parallelization-module-recap.en.srt |
2.09KB |
| 01_scaling-to-huge-datasets-through-parallelization-module-recap.en.txt |
1.34KB |
| 01_scaling-to-huge-datasets-through-parallelization-module-recap.mp4 |
5.77MB |
| 01_selecting-tuning-parameters-via-cross-validation.en.srt |
5.19KB |
| 01_selecting-tuning-parameters-via-cross-validation.en.txt |
3.25KB |
| 01_selecting-tuning-parameters-via-cross-validation.mp4 |
12.39MB |
| 01_shuffle-data-before-running-stochastic-gradient.en.srt |
2.98KB |
| 01_shuffle-data-before-running-stochastic-gradient.en.txt |
1.90KB |
| 01_shuffle-data-before-running-stochastic-gradient.mp4 |
5.89MB |
| 01_slides-presented-in-this-module_boosting-annotated.pdf |
2.21MB |
| 01_slides-presented-in-this-module_classification-annotated.pdf |
6.89MB |
| 01_slides-presented-in-this-module_closing.pdf |
19.47MB |
| 01_slides-presented-in-this-module_closing.pdf |
4.35MB |
| 01_slides-presented-in-this-module_closing-annotated.pdf |
26.07MB |
| 01_slides-presented-in-this-module_clustering-intro-annotated.pdf |
14.07MB |
| 01_slides-presented-in-this-module_Dalal-cvpr05.pdf |
445.45KB |
| 01_slides-presented-in-this-module_decision-trees-annotated.pdf |
2.85MB |
| 01 slides-presented-in-this-module decision-trees-missing-values-annotated pdf |
1.57MB |
| 01_slides-presented-in-this-module_decision-trees-overfitting-annotated.pdf |
3.10MB |
| 01_slides-presented-in-this-module_deeplearning-annotated.pdf |
10.85MB |
| 01_slides-presented-in-this-module_eccv06.pdf |
706.42KB |
| 01_slides-presented-in-this-module_iccv99.pdf |
564.40KB |
| 01_slides-presented-in-this-module_imagenet.pdf |
1.35MB |
| 01_slides-presented-in-this-module_instructions.html |
3.11KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.14KB |
| 01_slides-presented-in-this-module_instructions.html |
1.14KB |
| 01_slides-presented-in-this-module_instructions.html |
1.13KB |
| 01_slides-presented-in-this-module_instructions.html |
1.16KB |
| 01_slides-presented-in-this-module_instructions.html |
1.14KB |
| 01_slides-presented-in-this-module_instructions.html |
1.12KB |
| 01_slides-presented-in-this-module_instructions.html |
1.12KB |
| 01_slides-presented-in-this-module_instructions.html |
1.14KB |
| 01_slides-presented-in-this-module_instructions.html |
1.14KB |
| 01_slides-presented-in-this-module_instructions.html |
1.13KB |
| 01_slides-presented-in-this-module_instructions.html |
1.14KB |
| 01_slides-presented-in-this-module_instructions.html |
1.13KB |
| 01_slides-presented-in-this-module_instructions.html |
1.13KB |
| 01_slides-presented-in-this-module_instructions.html |
1.17KB |
| 01_slides-presented-in-this-module_instructions.html |
1.16KB |
| 01_slides-presented-in-this-module_instructions.html |
1.17KB |
| 01_slides-presented-in-this-module_instructions.html |
1.17KB |
| 01_slides-presented-in-this-module_instructions.html |
1.13KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.15KB |
| 01_slides-presented-in-this-module_instructions.html |
1.13KB |
| 01_slides-presented-in-this-module_johnson_andrew_1997_3.pdf |
8.91MB |
| 01_slides-presented-in-this-module_kmeans-annotated.pdf |
10.90MB |
| 01_slides-presented-in-this-module_LDA-annotated.pdf |
6.95MB |
| 01_slides-presented-in-this-module_LM-3dtexton.pdf |
1.81MB |
| 01 slides-presented-in-this-module logistic-regression-learning-annotated pdf |
3.01MB |
| 01_slides-presented-in-this-module_logistic-regression-model-annotated.pdf |
11.49MB |
| 01 slides-presented-in-this-module logistic-regression-overfitting-annotated pdf |
2.56MB |
| 01_slides-presented-in-this-module_mikolajczyk_pami05.pdf |
2.00MB |
| 01_slides-presented-in-this-module_mixmodel-EM-annotated.pdf |
17.68MB |
| 01_slides-presented-in-this-module_online-learning-annotated.pdf |
3.65MB |
| 01_slides-presented-in-this-module_precision-recall.pdf |
1.93MB |
| 01_slides-presented-in-this-module_recommenders-intro-annotated.pdf |
12.33MB |
| 01_slides-presented-in-this-module_regression-intro-annotated.pdf |
21.53MB |
| 01_slides-presented-in-this-module_retrieval-intro-annotated.pdf |
13.56MB |
| 01_slides-presented-in-this-module_week1_simpleregression-annotated.pdf |
7.94MB |
| 01_slides-presented-in-this-module_week2_multipleregression-annotated.pdf |
10.12MB |
| 01_slides-presented-in-this-module_week3_assessingperformance-annotated.pdf |
6.82MB |
| 01_slides-presented-in-this-module_week4_ridgeregression-annotated.pdf |
3.01MB |
| 01_slides-presented-in-this-module_week5_lassoregression-annotated.pdf |
8.74MB |
| 01_slides-presented-in-this-module_week6_NNkernelregression-annotated.pdf |
4.53MB |
| 01_sparse-logistic-regression-with-l1-regularization.en.srt |
9.15KB |
| 01_sparse-logistic-regression-with-l1-regularization.en.txt |
5.60KB |
| 01_sparse-logistic-regression-with-l1-regularization.mp4 |
20.75MB |
| 01_state-action-value-function-definition.en.srt |
13.52KB |
| 01_state-action-value-function-definition.en.txt |
8.30KB |
| 01_state-action-value-function-definition.mp4 |
19.84MB |
| 01_summary-and-thank-you.en.srt |
5.27KB |
| 01_summary-and-thank-you.en.txt |
2.79KB |
| 01_summary-and-thank-you.mp4 |
13.94MB |
| 01_tensorflow-implementation.en.srt |
5.94KB |
| 01_tensorflow-implementation.en.txt |
3.09KB |
| 01_tensorflow-implementation.mp4 |
11.40MB |
| 01_the-boosting-theorem.en.srt |
5.47KB |
| 01_the-boosting-theorem.en.txt |
3.46KB |
| 01_the-boosting-theorem.mp4 |
10.85MB |
| 01_the-k-means-algorithm.en.srt |
7.91KB |
| 01_the-k-means-algorithm.en.txt |
4.87KB |
| 01_the-k-means-algorithm.mp4 |
17.25MB |
| 01_the-matrix-completion-task.en.srt |
6.45KB |
| 01_the-matrix-completion-task.en.txt |
4.03KB |
| 01_the-matrix-completion-task.mp4 |
14.78MB |
| 01_the-need-for-bayesian-inference.en.srt |
6.75KB |
| 01_the-need-for-bayesian-inference.en.txt |
4.33KB |
| 01_the-need-for-bayesian-inference.mp4 |
17.01MB |
| 01_the-online-learning-task.en.srt |
5.15KB |
| 01_the-online-learning-task.en.txt |
3.14KB |
| 01_the-online-learning-task.mp4 |
10.21MB |
| 01_the-problem-of-overfitting.en.srt |
18.32KB |
| 01_the-problem-of-overfitting.en.txt |
9.57KB |
| 01_the-problem-of-overfitting.mp4 |
23.97MB |
| 01_the-simple-linear-regression-model.en.srt |
2.64KB |
| 01_the-simple-linear-regression-model.en.txt |
1.62KB |
| 01_the-simple-linear-regression-model.mp4 |
7.65MB |
| 01_threshold-splits-for-continuous-inputs.en.srt |
7.93KB |
| 01_threshold-splits-for-continuous-inputs.en.txt |
4.88KB |
| 01_threshold-splits-for-continuous-inputs.mp4 |
17.28MB |
| 01_training-and-evaluating-a-classifier.en.srt |
5.98KB |
| 01_training-and-evaluating-a-classifier.en.txt |
3.66KB |
| 01_training-and-evaluating-a-classifier.mp4 |
12.95MB |
| 01_training-error-assessing-loss-on-the-training-set.en.srt |
10.27KB |
| 01_training-error-assessing-loss-on-the-training-set.en.txt |
6.51KB |
| 01_training-error-assessing-loss-on-the-training-set.mp4 |
20.37MB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent amazon baby subset csv zip |
9.16MB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent amazon baby subset gl zip |
12.73MB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent amazon baby subset sframe zip |
12.73MB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent CLA10-NB01 ipynb zip |
12.72KB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent important words json zip |
862B |
| 01 training-logistic-regression-via-stochastic-gradient-ascent instructions html |
126.40KB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent module-10-assignment-numpy-arrays npz zip |
1.16MB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent module-10-assignment-train-idx json zip |
53.07KB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent module-10-assignment-validation-idx json zip |
10.24KB |
| 01 training-logistic-regression-via-stochastic-gradient-ascent module-10-online-learning-assignment-blank ipynb zip |
8.77KB |
| 01 training-validation-test-split-for-model-selection-fitting-and-assessment en srt |
10.29KB |
| 01 training-validation-test-split-for-model-selection-fitting-and-assessment en txt |
6.43KB |
| 01 training-validation-test-split-for-model-selection-fitting-and-assessment mp4 |
25.37MB |
| 01_using-lasso-to-select-features_home_data.sframe.zip |
908.17KB |
| 01_using-lasso-to-select-features_instructions.html |
13.76KB |
| 01_using-lasso-to-select-features_kc_house_data.csv.zip |
780.66KB |
| 01_using-lasso-to-select-features_numpy-tutorial-py3.ipynb.zip |
2.92KB |
| 01_using-lasso-to-select-features_REG05-NB01.ipynb.zip |
3.94KB |
| 01_using-lasso-to-select-features_wk3_kc_house_test_data.csv.zip |
81.70KB |
| 01_using-lasso-to-select-features_wk3_kc_house_train_data.csv.zip |
354.68KB |
| 01_using-lasso-to-select-features_wk3_kc_house_valid_data.csv.zip |
349.96KB |
| 01_using-multiple-decision-trees.en.srt |
6.44KB |
| 01_using-multiple-decision-trees.en.txt |
3.39KB |
| 01_using-multiple-decision-trees.mp4 |
12.51MB |
| 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.en.srt |
5.59KB |
| 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.en.txt |
3.37KB |
| 01_very-optional-deriving-gradient-of-logistic-regression-log-trick.mp4 |
12.89MB |
| 01_visualizing-the-ridge-cost.en.srt |
8.21KB |
| 01_visualizing-the-ridge-cost.en.txt |
4.87KB |
| 01_visualizing-the-ridge-cost.mp4 |
17.83MB |
| 01_week-2-practice-lab-linear-regression_instructions.html |
2.55KB |
| 01_week-3-practice-lab-logistic-regression_instructions.html |
1.07KB |
| 01_welcome.en.srt |
5.46KB |
| 01_welcome.en.srt |
5.36KB |
| 01_welcome.en.txt |
2.84KB |
| 01_welcome.en.txt |
2.91KB |
| 01_welcome.mp4 |
10.64MB |
| 01_welcome.mp4 |
8.27MB |
| 01_welcome-to-machine-learning.en.srt |
3.91KB |
| 01_welcome-to-machine-learning.en.txt |
2.40KB |
| 01_welcome-to-machine-learning.mp4 |
22.19MB |
| 01_what-is-a-derivative-optional.en.srt |
29.72KB |
| 01_what-is-a-derivative-optional.en.txt |
15.20KB |
| 01_what-is-a-derivative-optional.mp4 |
38.30MB |
| 01_what-is-clustering.en.srt |
5.90KB |
| 01_what-is-clustering.en.txt |
3.15KB |
| 01_what-is-clustering.mp4 |
8.82MB |
| 01_what-is-collapsed-gibbs-sampling.en.srt |
4.39KB |
| 01_what-is-collapsed-gibbs-sampling.en.txt |
2.88KB |
| 01_what-is-collapsed-gibbs-sampling.mp4 |
11.71MB |
| 01_what-is-machine-learning.en.srt |
9.46KB |
| 01_what-is-machine-learning.en.txt |
4.96KB |
| 01_what-is-machine-learning.mp4 |
25.98MB |
| 01_what-is-reinforcement-learning.en.srt |
12.51KB |
| 01_what-is-reinforcement-learning.en.txt |
8.00KB |
| 01_what-is-reinforcement-learning.mp4 |
30.97MB |
| 01_what-makes-the-lasso-objective-different.en.srt |
3.96KB |
| 01_what-makes-the-lasso-objective-different.en.txt |
2.41KB |
| 01_what-makes-the-lasso-objective-different.mp4 |
8.39MB |
| 01_what-we-covered-and-what-we-didn-t-cover.en.srt |
8.46KB |
| 01_what-we-covered-and-what-we-didn-t-cover.en.txt |
5.35KB |
| 01_what-we-covered-and-what-we-didn-t-cover.mp4 |
16.55MB |
| 01_what-we-didn-t-cover.en.srt |
4.05KB |
| 01_what-we-didn-t-cover.en.txt |
2.57KB |
| 01_what-we-didn-t-cover.mp4 |
8.60MB |
| 01_why-gradient-ascent-won-t-scale.en.srt |
4.79KB |
| 01_why-gradient-ascent-won-t-scale.en.txt |
2.88KB |
| 01_why-gradient-ascent-won-t-scale.mp4 |
8.91MB |
| 01_why-hierarchical-clustering.en.srt |
3.69KB |
| 01_why-hierarchical-clustering.en.txt |
2.37KB |
| 01_why-hierarchical-clustering.mp4 |
7.70MB |
| 01_why-would-stochastic-gradient-ever-work.en.srt |
5.08KB |
| 01_why-would-stochastic-gradient-ever-work.en.txt |
3.06KB |
| 01_why-would-stochastic-gradient-ever-work.mp4 |
10.36MB |
| 02_a-brief-recap.en.srt |
1.88KB |
| 02_a-brief-recap.en.srt |
4.74KB |
| 02_a-brief-recap.en.srt |
1.96KB |
| 02_a-brief-recap.en.txt |
1.16KB |
| 02_a-brief-recap.en.txt |
2.99KB |
| 02_a-brief-recap.en.txt |
1.14KB |
| 02_a-brief-recap.mp4 |
11.18MB |
| 02_a-brief-recap.mp4 |
5.01MB |
| 02_a-brief-recap.mp4 |
3.73MB |
| 02_a-case-study-in-predicting-house-prices.en.srt |
1.50KB |
| 02_a-case-study-in-predicting-house-prices.en.txt |
934B |
| 02_a-case-study-in-predicting-house-prices.mp4 |
3.81MB |
| 02_additional-layer-types.en.srt |
11.31KB |
| 02_additional-layer-types.en.txt |
7.24KB |
| 02_additional-layer-types.mp4 |
19.53MB |
| 02_addressing-overfitting.en.srt |
12.87KB |
| 02_addressing-overfitting.en.txt |
6.85KB |
| 02_addressing-overfitting.mp4 |
15.73MB |
| 02_analyzing-product-sentiment_exam.html |
12.21KB |
| 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.srt |
886B |
| 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.en.txt |
522B |
| 02_analyzing-the-sentiment-of-reviews-a-case-study-in-classification.mp4 |
2.85MB |
| 02_applications-of-machine-learning.en.srt |
7.48KB |
| 02_applications-of-machine-learning.en.txt |
3.93KB |
| 02_applications-of-machine-learning.mp4 |
33.45MB |
| 02_approach-1-closed-form-solution.en.srt |
6.58KB |
| 02_approach-1-closed-form-solution.en.srt |
5.49KB |
| 02_approach-1-closed-form-solution.en.srt |
4.39KB |
| 02_approach-1-closed-form-solution.en.txt |
3.90KB |
| 02_approach-1-closed-form-solution.en.txt |
3.18KB |
| 02_approach-1-closed-form-solution.en.txt |
2.64KB |
| 02_approach-1-closed-form-solution.mp4 |
14.07MB |
| 02_approach-1-closed-form-solution.mp4 |
13.31MB |
| 02_approach-1-closed-form-solution.mp4 |
9.99MB |
| 02_a-review-of-overfitting.en.srt |
3.46KB |
| 02_a-review-of-overfitting.en.txt |
2.24KB |
| 02_a-review-of-overfitting.mp4 |
7.18MB |
| 02_assessing-convergence-and-other-lasso-solvers.en.srt |
3.84KB |
| 02_assessing-convergence-and-other-lasso-solvers.en.txt |
2.42KB |
| 02_assessing-convergence-and-other-lasso-solvers.mp4 |
9.33MB |
| 02_assessing-performance-intro.en.srt |
760B |
| 02_assessing-performance-intro.en.txt |
464B |
| 02_assessing-performance-intro.mp4 |
1.77MB |
| 02_a-worked-example-for-lda-initial-setup.en.srt |
4.90KB |
| 02_a-worked-example-for-lda-initial-setup.en.txt |
3.02KB |
| 02_a-worked-example-for-lda-initial-setup.mp4 |
8.78MB |
| 02_careful-with-step-sizes-that-are-too-large.en.srt |
5.29KB |
| 02_careful-with-step-sizes-that-are-too-large.en.txt |
3.24KB |
| 02_careful-with-step-sizes-that-are-too-large.mp4 |
11.16MB |
| 02_case-study-where-accuracy-is-not-best-metric-for-classification.en.srt |
5.59KB |
| 02_case-study-where-accuracy-is-not-best-metric-for-classification.en.txt |
3.48KB |
| 02_case-study-where-accuracy-is-not-best-metric-for-classification.mp4 |
12.61MB |
| 02_challenge-of-missing-data.en.srt |
5.55KB |
| 02_challenge-of-missing-data.en.txt |
3.53KB |
| 02_challenge-of-missing-data.mp4 |
12.63MB |
| 02_choosing-activation-functions.en.srt |
13.76KB |
| 02_choosing-activation-functions.en.txt |
7.24KB |
| 02_choosing-activation-functions.mp4 |
23.39MB |
| 02_choosing-a-split-information-gain.en.srt |
17.43KB |
| 02_choosing-a-split-information-gain.en.txt |
8.96KB |
| 02_choosing-a-split-information-gain.mp4 |
23.77MB |
| 02_choosing-step-size.en.srt |
4.42KB |
| 02_choosing-step-size.en.txt |
2.77KB |
| 02_choosing-step-size.mp4 |
9.64MB |
| 02_classification_exam.html |
35.57KB |
| 02_clustering-and-similarity_exam.html |
109.15KB |
| 02_clustering-documents-an-unsupervised-learning-task.en.srt |
5.53KB |
| 02_clustering-documents-an-unsupervised-learning-task.en.txt |
3.45KB |
| 02_clustering-documents-an-unsupervised-learning-task.mp4 |
11.27MB |
| 02_computation-graph-optional.en.srt |
26.39KB |
| 02_computation-graph-optional.en.txt |
13.61KB |
| 02_computation-graph-optional.mp4 |
29.97MB |
| 02_convergence-initialization-and-overfitting-of-em.en.srt |
12.47KB |
| 02_convergence-initialization-and-overfitting-of-em.en.txt |
8.03KB |
| 02_convergence-initialization-and-overfitting-of-em.mp4 |
29.91MB |
| 02_convergence-paths.en.srt |
2.77KB |
| 02_convergence-paths.en.txt |
1.72KB |
| 02_convergence-paths.mp4 |
7.09MB |
| 02_coordinate-descent.en.srt |
7.03KB |
| 02_coordinate-descent.en.txt |
4.32KB |
| 02_coordinate-descent.mp4 |
15.11MB |
| 02_data-in-tensorflow.en.srt |
13.17KB |
| 02_data-in-tensorflow.en.txt |
8.25KB |
| 02_data-in-tensorflow.mp4 |
24.83MB |
| 02_deep-features-for-image-retrieval_exam.html |
165.09KB |
| 02_deep-learning_exam.html |
1.26MB |
| 02_deep-learning-for-content-based-filtering.en.srt |
14.18KB |
| 02_deep-learning-for-content-based-filtering.en.txt |
7.42KB |
| 02_deep-learning-for-content-based-filtering.mp4 |
24.34MB |
| 02_deep-learning-performance.en.srt |
4.01KB |
| 02_deep-learning-performance.en.txt |
2.46KB |
| 02_deep-learning-performance.mp4 |
13.24MB |
| 02_defining-notation.en.srt |
4.34KB |
| 02_defining-notation.en.txt |
2.73KB |
| 02_defining-notation.mp4 |
11.87MB |
| 02_distance-metrics.en.srt |
5.70KB |
| 02_distance-metrics.en.txt |
3.69KB |
| 02_distance-metrics.mp4 |
14.18MB |
| 02_distance-metrics-euclidean-and-scaled-euclidean.en.srt |
9.24KB |
| 02_distance-metrics-euclidean-and-scaled-euclidean.en.txt |
6.06KB |
| 02_distance-metrics-euclidean-and-scaled-euclidean.mp4 |
21.05MB |
| 02_divisive-clustering.en.srt |
6.21KB |
| 02_divisive-clustering.en.txt |
3.96KB |
| 02_divisive-clustering.mp4 |
13.08MB |
| 02 document-retrieval-a-case-study-in-clustering-and-measuring-similarity en srt |
858B |
| 02 document-retrieval-a-case-study-in-clustering-and-measuring-similarity en txt |
528B |
| 02 document-retrieval-a-case-study-in-clustering-and-measuring-similarity mp4 |
2.08MB |
| 02_early-stopping-in-learning-decision-trees.en.srt |
9.41KB |
| 02_early-stopping-in-learning-decision-trees.en.txt |
5.83KB |
| 02_early-stopping-in-learning-decision-trees.mp4 |
22.21MB |
| 02_effect-of-popular-items.en.srt |
3.84KB |
| 02_effect-of-popular-items.en.txt |
2.29KB |
| 02_effect-of-popular-items.mp4 |
8.05MB |
| 02_error-analysis.en.srt |
13.23KB |
| 02_error-analysis.en.txt |
7.05KB |
| 02_error-analysis.mp4 |
17.51MB |
| 02_evaluating-a-classifier.en.srt |
4.58KB |
| 02_evaluating-a-classifier.en.txt |
2.87KB |
| 02_evaluating-a-classifier.mp4 |
9.26MB |
| 02_evaluating-a-model.en.srt |
12.81KB |
| 02_evaluating-a-model.en.txt |
8.14KB |
| 02_evaluating-a-model.mp4 |
19.45MB |
| 02_feature-scaling-part-2.en.srt |
10.81KB |
| 02_feature-scaling-part-2.en.txt |
5.58KB |
| 02_feature-scaling-part-2.mp4 |
14.39MB |
| 02_feature-split-selection-with-missing-data.en.srt |
7.16KB |
| 02_feature-split-selection-with-missing-data.en.txt |
4.45KB |
| 02_feature-split-selection-with-missing-data.mp4 |
16.06MB |
| 02_finding-maxima-or-minima-analytically.en.srt |
7.23KB |
| 02_finding-maxima-or-minima-analytically.en.txt |
4.28KB |
| 02_finding-maxima-or-minima-analytically.mp4 |
16.85MB |
| 02_formally-deriving-why-3-sources-of-error.en.srt |
21.77KB |
| 02_formally-deriving-why-3-sources-of-error.en.txt |
13.06KB |
| 02_formally-deriving-why-3-sources-of-error.mp4 |
46.69MB |
| 02_gaussian-normal-distribution.en.srt |
14.96KB |
| 02_gaussian-normal-distribution.en.txt |
7.86KB |
| 02_gaussian-normal-distribution.mp4 |
20.88MB |
| 02_general-implementation-of-forward-propagation.en.srt |
11.61KB |
| 02_general-implementation-of-forward-propagation.en.txt |
6.01KB |
| 02_general-implementation-of-forward-propagation.mp4 |
21.33MB |
| 02_generalization-error-what-we-really-want.en.srt |
10.29KB |
| 02_generalization-error-what-we-really-want.en.txt |
6.54KB |
| 02_generalization-error-what-we-really-want.mp4 |
21.76MB |
| 02_gibbs-sampling-from-10-000-feet.en.srt |
7.45KB |
| 02_gibbs-sampling-from-10-000-feet.en.txt |
4.80KB |
| 02_gibbs-sampling-from-10-000-feet.mp4 |
18.44MB |
| 02 global-fits-of-parametric-models-vs-local-fits-of-kernel-regression en srt |
8.46KB |
| 02 global-fits-of-parametric-models-vs-local-fits-of-kernel-regression en txt |
5.42KB |
| 02_global-fits-of-parametric-models-vs-local-fits-of-kernel-regression.mp4 |
20.08MB |
| 02_goal-learning-parameters-of-logistic-regression.en.srt |
2.91KB |
| 02_goal-learning-parameters-of-logistic-regression.en.txt |
1.85KB |
| 02_goal-learning-parameters-of-logistic-regression.mp4 |
8.84MB |
| 02_gradient-ascent-won-t-scale-to-todays-huge-datasets.en.srt |
5.11KB |
| 02_gradient-ascent-won-t-scale-to-todays-huge-datasets.en.txt |
3.19KB |
| 02_gradient-ascent-won-t-scale-to-todays-huge-datasets.mp4 |
11.97MB |
| 02_gradient-descent-multidimensional-hill-descent.en.srt |
6.20KB |
| 02_gradient-descent-multidimensional-hill-descent.en.txt |
3.69KB |
| 02_gradient-descent-multidimensional-hill-descent.mp4 |
16.87MB |
| 02_implementing-gradient-descent.en.srt |
14.24KB |
| 02_implementing-gradient-descent.en.txt |
7.51KB |
| 02_implementing-gradient-descent.mp4 |
20.91MB |
| 02 implementing-gradient-descent-for-multiple-regression home data sframe zip |
908.17KB |
| 02_implementing-gradient-descent-for-multiple-regression_instructions.html |
14.94KB |
| 02 implementing-gradient-descent-for-multiple-regression kc house data csv zip |
780.66KB |
| 02 implementing-gradient-descent-for-multiple-regression kc house test data csv zip |
154.80KB |
| 02 implementing-gradient-descent-for-multiple-regression kc house train data csv zip |
628.45KB |
| 02 implementing-gradient-descent-for-multiple-regression numpy-tutorial-py3 ipynb zip ipynb |
2.92KB |
| 02 implementing-gradient-descent-for-multiple-regression REG02-NB02 ipynb zip |
5.65KB |
| 02_influence-of-high-leverage-points-exploring-the-data.en.srt |
5.66KB |
| 02_influence-of-high-leverage-points-exploring-the-data.en.txt |
3.58KB |
| 02_influence-of-high-leverage-points-exploring-the-data.mp4 |
11.92MB |
| 02_interpreting-the-mixture-of-gaussian-terms.en.srt |
6.79KB |
| 02_interpreting-the-mixture-of-gaussian-terms.en.txt |
4.18KB |
| 02_interpreting-the-mixture-of-gaussian-terms.mp4 |
14.38MB |
| 02 issues-with-high-dimensions-data-scarcity-and-computational-complexity en srt |
4.51KB |
| 02 issues-with-high-dimensions-data-scarcity-and-computational-complexity en txt |
2.86KB |
| 02 issues-with-high-dimensions-data-scarcity-and-computational-complexity mp4 |
9.80MB |
| 02_kd-tree-representation.en.srt |
10.16KB |
| 02_kd-tree-representation.en.txt |
6.30KB |
| 02_kd-tree-representation.mp4 |
22.57MB |
| 02_k-fold-cross-validation.en.srt |
7.29KB |
| 02_k-fold-cross-validation.en.txt |
4.68KB |
| 02_k-fold-cross-validation.mp4 |
17.01MB |
| 02_k-means-as-coordinate-descent.en.srt |
8.42KB |
| 02_k-means-as-coordinate-descent.en.txt |
5.34KB |
| 02_k-means-as-coordinate-descent.mp4 |
17.76MB |
| 02_k-means-intuition.en.srt |
9.00KB |
| 02_k-means-intuition.en.txt |
5.66KB |
| 02_k-means-intuition.mp4 |
12.36MB |
| 02_k-nearest-neighbors-in-practice.en.srt |
4.89KB |
| 02_k-nearest-neighbors-in-practice.en.txt |
3.17KB |
| 02_k-nearest-neighbors-in-practice.mp4 |
11.67MB |
| 02_l2-regularized-logistic-regression.en.srt |
5.96KB |
| 02_l2-regularized-logistic-regression.en.txt |
3.68KB |
| 02_l2-regularized-logistic-regression.mp4 |
12.49MB |
| 02_learning-a-decision-stump.en.srt |
5.10KB |
| 02_learning-a-decision-stump.en.txt |
3.18KB |
| 02_learning-a-decision-stump.mp4 |
12.90MB |
| 02_learning-algorithm-for-logistic-regression.en.srt |
4.14KB |
| 02_learning-algorithm-for-logistic-regression.en.txt |
2.55KB |
| 02_learning-algorithm-for-logistic-regression.mp4 |
8.00MB |
| 02_learning-boosted-decision-stumps-with-adaboost.en.srt |
6.20KB |
| 02_learning-boosted-decision-stumps-with-adaboost.en.txt |
3.92KB |
| 02_learning-boosted-decision-stumps-with-adaboost.mp4 |
13.99MB |
| 02_learning-process.en.srt |
18.03KB |
| 02_learning-process.en.txt |
9.42KB |
| 02_learning-process.mp4 |
29.03MB |
| 02_limitations-of-parametric-regression.en.srt |
5.25KB |
| 02_limitations-of-parametric-regression.en.txt |
3.30KB |
| 02_limitations-of-parametric-regression.mp4 |
11.29MB |
| 02_linear-classifiers-a-motivating-example.en.srt |
3.88KB |
| 02_linear-classifiers-a-motivating-example.en.txt |
2.36KB |
| 02_linear-classifiers-a-motivating-example.mp4 |
9.90MB |
| 02_linear-regression-model-part-2.en.srt |
9.57KB |
| 02_linear-regression-model-part-2.en.txt |
5.03KB |
| 02_linear-regression-model-part-2.mp4 |
16.22MB |
| 02_loading-and-exploring-song-data.en.srt |
6.16KB |
| 02_loading-and-exploring-song-data.en.txt |
3.58KB |
| 02_loading-and-exploring-song-data.mp4 |
14.79MB |
| 02_loading-exploring-house-sale-data.en.srt |
7.88KB |
| 02_loading-exploring-house-sale-data.en.txt |
4.87KB |
| 02_loading-exploring-house-sale-data.mp4 |
18.41MB |
| 02_loading-exploring-product-review-data.en.srt |
3.28KB |
| 02_loading-exploring-product-review-data.en.txt |
1.89KB |
| 02_loading-exploring-product-review-data.mp4 |
8.00MB |
| 02_loading-exploring-wikipedia-data.en.srt |
5.35KB |
| 02_loading-exploring-wikipedia-data.en.txt |
3.10KB |
| 02_loading-exploring-wikipedia-data.mp4 |
17.54MB |
| 02_loading-image-data.en.srt |
4.07KB |
| 02_loading-image-data.en.srt |
3.20KB |
| 02_loading-image-data.en.txt |
2.39KB |
| 02_loading-image-data.en.txt |
1.95KB |
| 02_loading-image-data.mp4 |
9.42MB |
| 02_loading-image-data.mp4 |
7.42MB |
| 02_logistic-regression.en.srt |
13.34KB |
| 02_logistic-regression.en.txt |
7.02KB |
| 02_logistic-regression.mp4 |
21.48MB |
| 02_lsh-as-an-alternative-to-kd-trees.en.srt |
6.05KB |
| 02_lsh-as-an-alternative-to-kd-trees.en.txt |
3.90KB |
| 02_lsh-as-an-alternative-to-kd-trees.mp4 |
12.85MB |
| 02_lunar-lander.en.srt |
7.76KB |
| 02_lunar-lander.en.txt |
4.92KB |
| 02_lunar-lander.mp4 |
10.37MB |
| 02_mars-rover-example.en.srt |
10.32KB |
| 02_mars-rover-example.en.txt |
5.33KB |
| 02_mars-rover-example.mp4 |
12.65MB |
| 02_matrix-multiplication.en.srt |
12.33KB |
| 02_matrix-multiplication.en.txt |
6.39KB |
| 02_matrix-multiplication.mp4 |
15.89MB |
| 02_mixed-membership-models-for-documents.en.srt |
4.76KB |
| 02_mixed-membership-models-for-documents.en.txt |
3.04KB |
| 02_mixed-membership-models-for-documents.mp4 |
13.22MB |
| 02_module-1-recap.en.srt |
15.55KB |
| 02_module-1-recap.en.txt |
9.99KB |
| 02_module-1-recap.mp4 |
29.31MB |
| 02_more-complex-neural-networks.en.srt |
11.73KB |
| 02_more-complex-neural-networks.en.txt |
6.06KB |
| 02_more-complex-neural-networks.mp4 |
17.03MB |
| 02_motiving-probabilistic-clustering-models.en.srt |
10.74KB |
| 02_motiving-probabilistic-clustering-models.en.txt |
6.85KB |
| 02_motiving-probabilistic-clustering-models.mp4 |
22.35MB |
| 02_multiclass-classification-with-1-versus-all.en.srt |
9.77KB |
| 02_multiclass-classification-with-1-versus-all.en.txt |
6.05KB |
| 02_multiclass-classification-with-1-versus-all.mp4 |
22.67MB |
| 02_multiclass-classification-with-decision-trees.en.srt |
3.62KB |
| 02_multiclass-classification-with-decision-trees.en.txt |
2.30KB |
| 02_multiclass-classification-with-decision-trees.mp4 |
7.00MB |
| 02_multiple-regression-intro.en.srt |
643B |
| 02_multiple-regression-intro.en.txt |
380B |
| 02_multiple-regression-intro.mp4 |
1.21MB |
| 02_neurons-and-the-brain.en.srt |
18.27KB |
| 02_neurons-and-the-brain.en.txt |
9.52KB |
| 02_neurons-and-the-brain.mp4 |
26.86MB |
| 02_optimal-recommenders.en.srt |
2.67KB |
| 02_optimal-recommenders.en.txt |
1.52KB |
| 02_optimal-recommenders.mp4 |
5.88MB |
| 02_optional-opportunity-to-mentor-other-learners_instructions.html |
1.66KB |
| 02_optional-picking-the-best-threshold-to-split-on.en.srt |
4.08KB |
| 02_optional-picking-the-best-threshold-to-split-on.en.txt |
2.53KB |
| 02_optional-picking-the-best-threshold-to-split-on.mp4 |
9.47MB |
| 02_optional-pruning-decision-trees-to-avoid-overfitting.en.srt |
7.53KB |
| 02_optional-pruning-decision-trees-to-avoid-overfitting.en.txt |
4.65KB |
| 02_optional-pruning-decision-trees-to-avoid-overfitting.mp4 |
16.72MB |
| 02_optional-responsibilities-as-bayes-rule.en.srt |
6.12KB |
| 02_optional-responsibilities-as-bayes-rule.en.txt |
3.84KB |
| 02_optional-responsibilities-as-bayes-rule.mp4 |
14.41MB |
| 02_outline-of-first-half-of-course.en.srt |
8.41KB |
| 02_outline-of-first-half-of-course.en.txt |
5.40KB |
| 02_outline-of-first-half-of-course.mp4 |
19.39MB |
| 02_overfitting-in-boosting.en.srt |
6.93KB |
| 02_overfitting-in-boosting.en.txt |
4.29KB |
| 02_overfitting-in-boosting.mp4 |
14.59MB |
| 02_pca-algorithm-optional.en.srt |
24.43KB |
| 02_pca-algorithm-optional.en.txt |
12.81KB |
| 02_pca-algorithm-optional.mp4 |
28.01MB |
| 02_predicting-house-prices_exam.html |
4.17KB |
| 02_predicting-house-prices-a-case-study-in-regression.en.srt |
2.03KB |
| 02_predicting-house-prices-a-case-study-in-regression.en.txt |
1.24KB |
| 02_predicting-house-prices-a-case-study-in-regression.mp4 |
3.83MB |
| 02_predicting-loan-defaults-with-decision-trees.en.srt |
6.15KB |
| 02_predicting-loan-defaults-with-decision-trees.en.txt |
3.87KB |
| 02_predicting-loan-defaults-with-decision-trees.mp4 |
13.74MB |
| 02_recall-fraction-of-positive-data-predicted-to-be-positive.en.srt |
4.26KB |
| 02_recall-fraction-of-positive-data-predicted-to-be-positive.en.txt |
2.68KB |
| 02_recall-fraction-of-positive-data-predicted-to-be-positive.mp4 |
10.56MB |
| 02_recommendations-from-known-user-item-features.en.srt |
6.53KB |
| 02_recommendations-from-known-user-item-features.en.txt |
3.85KB |
| 02_recommendations-from-known-user-item-features.mp4 |
14.18MB |
| 02_recommender-systems_exam.html |
789.13KB |
| 02_recommender-systems-overview.en.srt |
2.01KB |
| 02_recommender-systems-overview.en.txt |
1.29KB |
| 02_recommender-systems-overview.mp4 |
4.62MB |
| 02_recommending-songs_exam.html |
3.32KB |
| 02_regression_exam.html |
676.92KB |
| 02_regularization-and-bias-variance.en.srt |
16.26KB |
| 02_regularization-and-bias-variance.en.txt |
8.30KB |
| 02_regularization-and-bias-variance.mp4 |
21.11MB |
| 02_retrieval-as-k-nearest-neighbor-search.en.srt |
4.41KB |
| 02_retrieval-as-k-nearest-neighbor-search.en.txt |
2.73KB |
| 02_retrieval-as-k-nearest-neighbor-search.mp4 |
9.38MB |
| 02_retrieving-wikipedia-articles_exam.html |
8.34KB |
| 02_review-of-basics-of-probabilities.en.srt |
7.33KB |
| 02_review-of-basics-of-probabilities.en.txt |
4.36KB |
| 02_review-of-basics-of-probabilities.mp4 |
15.54MB |
| 02_rewriting-the-single-observation-model-in-vector-notation.en.srt |
7.80KB |
| 02_rewriting-the-single-observation-model-in-vector-notation.en.txt |
4.78KB |
| 02_rewriting-the-single-observation-model-in-vector-notation.mp4 |
15.44MB |
| 02_sampling-with-replacement.en.srt |
6.52KB |
| 02_sampling-with-replacement.en.txt |
3.41KB |
| 02_sampling-with-replacement.mp4 |
14.33MB |
| 02_searching-for-images-a-case-study-in-deep-learning.en.srt |
550B |
| 02_searching-for-images-a-case-study-in-deep-learning.en.txt |
320B |
| 02_searching-for-images-a-case-study-in-deep-learning.mp4 |
1.68MB |
| 02_sframes_exam.html |
3.39KB |
| 02_simple-and-multiple-regression.en.srt |
6.51KB |
| 02_simple-and-multiple-regression.en.txt |
4.19KB |
| 02_simple-and-multiple-regression.mp4 |
13.05MB |
| 02_simplified-cost-function-for-logistic-regression.en.srt |
7.63KB |
| 02_simplified-cost-function-for-logistic-regression.en.txt |
3.94KB |
| 02_simplified-cost-function-for-logistic-regression.mp4 |
11.74MB |
| 02_slides-presented-in-this-module_instructions.html |
1.12KB |
| 02_slides-presented-in-this-module_instructions.html |
1.12KB |
| 02_slides-presented-in-this-module_instructions.html |
1.13KB |
| 02_slides-presented-in-this-module_instructions.html |
1.12KB |
| 02_slides-presented-in-this-module_intro.pdf |
20.75MB |
| 02_slides-presented-in-this-module_intro.pdf |
10.79MB |
| 02_slides-presented-in-this-module_intro.pdf |
6.24MB |
| 02_slides-presented-in-this-module_intro.pdf |
6.24MB |
| 02_softmax.en.srt |
15.38KB |
| 02_softmax.en.txt |
7.93KB |
| 02_softmax.mp4 |
20.69MB |
| 02_starting-a-jupyter-notebook.en.srt |
7.58KB |
| 02_starting-a-jupyter-notebook.en.txt |
4.43KB |
| 02_starting-a-jupyter-notebook.mp4 |
13.77MB |
| 02_starting-turi-create-loading-an-sframe.en.srt |
5.36KB |
| 02_starting-turi-create-loading-an-sframe.en.txt |
3.18KB |
| 02_starting-turi-create-loading-an-sframe.mp4 |
11.53MB |
| 02_state-action-value-function-example.en.srt |
6.77KB |
| 02_state-action-value-function-example.en.txt |
4.25KB |
| 02_state-action-value-function-example.mp4 |
14.64MB |
| 02_stochastic-gradient-learning-one-data-point-at-a-time.en.srt |
4.39KB |
| 02_stochastic-gradient-learning-one-data-point-at-a-time.en.txt |
2.71KB |
| 02_stochastic-gradient-learning-one-data-point-at-a-time.mp4 |
8.82MB |
| 02_supervised-learning-part-1.en.srt |
10.79KB |
| 02_supervised-learning-part-1.en.txt |
5.63KB |
| 02_supervised-learning-part-1.mp4 |
13.87MB |
| 02_symptoms-of-overfitting-in-polynomial-regression.en.srt |
3.03KB |
| 02_symptoms-of-overfitting-in-polynomial-regression.en.txt |
1.91KB |
| 02_symptoms-of-overfitting-in-polynomial-regression.mp4 |
6.91MB |
| 02_tensorflow-implementation-of-collaborative-filtering.en.srt |
15.45KB |
| 02_tensorflow-implementation-of-collaborative-filtering.en.txt |
9.57KB |
| 02_tensorflow-implementation-of-collaborative-filtering.mp4 |
35.87MB |
| 02_thank-you.en.srt |
2.57KB |
| 02_thank-you.en.srt |
2.39KB |
| 02_thank-you.en.txt |
1.40KB |
| 02_thank-you.en.txt |
1.39KB |
| 02_thank-you.mp4 |
7.11MB |
| 02_thank-you.mp4 |
5.78MB |
| 02_the-boosting-question.en.srt |
5.85KB |
| 02_the-boosting-question.en.txt |
3.71KB |
| 02_the-boosting-question.mp4 |
13.24MB |
| 02_the-cost-of-using-a-given-line.en.srt |
7.87KB |
| 02_the-cost-of-using-a-given-line.en.txt |
4.89KB |
| 02_the-cost-of-using-a-given-line.mp4 |
17.45MB |
| 02_the-feature-selection-task.en.srt |
5.65KB |
| 02_the-feature-selection-task.en.txt |
3.53KB |
| 02_the-feature-selection-task.mp4 |
11.53MB |
| 02_the-general-mapreduce-abstraction.en.srt |
5.60KB |
| 02_the-general-mapreduce-abstraction.en.txt |
3.35KB |
| 02_the-general-mapreduce-abstraction.mp4 |
12.11MB |
| 02_the-goal-of-clustering.en.srt |
5.19KB |
| 02_the-goal-of-clustering.en.txt |
3.31KB |
| 02_the-goal-of-clustering.mp4 |
10.66MB |
| 02_the-resulting-ridge-objective-and-its-extreme-solutions.en.srt |
6.34KB |
| 02_the-resulting-ridge-objective-and-its-extreme-solutions.en.txt |
3.80KB |
| 02_the-resulting-ridge-objective-and-its-extreme-solutions.mp4 |
13.61MB |
| 02_the-sigmoid-or-logistic-link-function.en.srt |
5.59KB |
| 02_the-sigmoid-or-logistic-link-function.en.txt |
3.21KB |
| 02_the-sigmoid-or-logistic-link-function.mp4 |
12.01MB |
| 02_thresholding-ridge-coefficients.en.srt |
6.83KB |
| 02_thresholding-ridge-coefficients.en.txt |
4.25KB |
| 02_thresholding-ridge-coefficients.mp4 |
13.86MB |
| 02_trading-off-precision-and-recall.en.srt |
18.29KB |
| 02_trading-off-precision-and-recall.en.srt |
5.98KB |
| 02_trading-off-precision-and-recall.en.txt |
9.58KB |
| 02_trading-off-precision-and-recall.en.txt |
3.73KB |
| 02_trading-off-precision-and-recall.mp4 |
22.17MB |
| 02_trading-off-precision-and-recall.mp4 |
13.20MB |
| 02_training-details.en.srt |
20.89KB |
| 02_training-details.en.txt |
10.91KB |
| 02_training-details.mp4 |
24.08MB |
| 02_training-test-curves.en.srt |
5.51KB |
| 02_training-test-curves.en.txt |
3.33KB |
| 02_training-test-curves.mp4 |
9.46MB |
| 02_using-per-item-features.en.srt |
12.65KB |
| 02_using-per-item-features.en.txt |
7.76KB |
| 02_using-per-item-features.mp4 |
23.49MB |
| 02_using-stochastic-gradient-for-online-learning.en.srt |
5.78KB |
| 02_using-stochastic-gradient-for-online-learning.en.txt |
3.63KB |
| 02_using-stochastic-gradient-for-online-learning.mp4 |
14.39MB |
| 02_variance-and-the-bias-variance-tradeoff.en.srt |
8.25KB |
| 02_variance-and-the-bias-variance-tradeoff.en.txt |
4.98KB |
| 02_variance-and-the-bias-variance-tradeoff.mp4 |
18.96MB |
| 02_vectorization-part-1.en.srt |
9.69KB |
| 02_vectorization-part-1.en.txt |
5.08KB |
| 02_vectorization-part-1.mp4 |
17.27MB |
| 02_very-optional-expressing-the-log-likelihood.en.srt |
3.75KB |
| 02_very-optional-expressing-the-log-likelihood.en.txt |
2.26KB |
| 02_very-optional-expressing-the-log-likelihood.mp4 |
7.06MB |
| 02_visualizing-overconfident-predictions.en.srt |
5.55KB |
| 02_visualizing-overconfident-predictions.en.txt |
3.50KB |
| 02_visualizing-overconfident-predictions.mp4 |
10.19MB |
| 02_visualizing-the-ridge-solution.en.srt |
6.83KB |
| 02_visualizing-the-ridge-solution.en.txt |
4.01KB |
| 02_visualizing-the-ridge-solution.mp4 |
14.71MB |
| 02_weighted-error.en.srt |
6.74KB |
| 02_weighted-error.en.txt |
4.13KB |
| 02_weighted-error.mp4 |
13.15MB |
| 02_whats-a-good-accuracy.en.srt |
4.31KB |
| 02_whats-a-good-accuracy.en.txt |
2.70KB |
| 02_whats-a-good-accuracy.mp4 |
15.81MB |
| 02_where-is-ml-going.en.srt |
11.16KB |
| 02_where-is-ml-going.en.txt |
6.58KB |
| 02_where-is-ml-going.mp4 |
37.80MB |
| 02_where-should-my-files-go_instructions.html |
757.30KB |
| 02_who-is-this-specialization-for.en.srt |
5.56KB |
| 02_who-is-this-specialization-for.en.txt |
3.48KB |
| 02_who-is-this-specialization-for.mp4 |
14.35MB |
| 02_you-ve-made-it.en.srt |
1.04KB |
| 02_you-ve-made-it.en.txt |
636B |
| 02_you-ve-made-it.mp4 |
2.82MB |
| 03_1-nearest-neighbor-algorithm.en.srt |
4.82KB |
| 03_1-nearest-neighbor-algorithm.en.txt |
3.00KB |
| 03_1-nearest-neighbor-algorithm.mp4 |
10.84MB |
| 03_1-nn-algorithm.en.srt |
3.65KB |
| 03_1-nn-algorithm.en.txt |
2.33KB |
| 03_1-nn-algorithm.mp4 |
6.79MB |
| 03_adding-data.en.srt |
18.94KB |
| 03_adding-data.en.txt |
12.04KB |
| 03_adding-data.mp4 |
32.94MB |
| 03_adding-other-features.en.srt |
3.79KB |
| 03_adding-other-features.en.txt |
2.31KB |
| 03_adding-other-features.mp4 |
6.66MB |
| 03_agglomerative-clustering.en.srt |
3.97KB |
| 03_agglomerative-clustering.en.txt |
2.52KB |
| 03_agglomerative-clustering.mp4 |
8.65MB |
| 03_aggregating-over-unknown-classes-in-an-image-dataset.en.srt |
8.47KB |
| 03_aggregating-over-unknown-classes-in-an-image-dataset.en.txt |
5.43KB |
| 03_aggregating-over-unknown-classes-in-an-image-dataset.mp4 |
21.56MB |
| 03_all-subsets.en.srt |
7.18KB |
| 03_all-subsets.en.txt |
4.43KB |
| 03_all-subsets.mp4 |
13.65MB |
| 03_an-alternative-document-clustering-model.en.srt |
6.02KB |
| 03_an-alternative-document-clustering-model.en.txt |
3.90KB |
| 03_an-alternative-document-clustering-model.mp4 |
16.58MB |
| 03_anomaly-detection-algorithm.en.srt |
13.34KB |
| 03_anomaly-detection-algorithm.en.txt |
8.35KB |
| 03_anomaly-detection-algorithm.mp4 |
20.32MB |
| 03_an-unsupervised-task.en.srt |
7.58KB |
| 03_an-unsupervised-task.en.txt |
4.77KB |
| 03_an-unsupervised-task.mp4 |
16.48MB |
| 03_assessing-performance-and-ridge-regression.en.srt |
10.19KB |
| 03_assessing-performance-and-ridge-regression.en.txt |
6.54KB |
| 03_assessing-performance-and-ridge-regression.mp4 |
20.72MB |
| 03_a-standard-gibbs-sampler-for-lda.en.srt |
11.40KB |
| 03_a-standard-gibbs-sampler-for-lda.en.txt |
7.17KB |
| 03_a-standard-gibbs-sampler-for-lda.mp4 |
29.27MB |
| 03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.srt |
9.54KB |
| 03_a-worked-example-for-lda-deriving-the-resampling-distribution.en.txt |
6.09KB |
| 03_a-worked-example-for-lda-deriving-the-resampling-distribution.mp4 |
16.69MB |
| 03_bellman-equation.en.srt |
17.73KB |
| 03_bellman-equation.en.txt |
9.22KB |
| 03_bellman-equation.mp4 |
26.66MB |
| 03_building-a-neural-network.en.srt |
10.69KB |
| 03_building-a-neural-network.en.txt |
6.87KB |
| 03_building-a-neural-network.mp4 |
24.40MB |
| 03_canvas-for-data-visualization.en.srt |
5.18KB |
| 03_canvas-for-data-visualization.en.txt |
3.17KB |
| 03_canvas-for-data-visualization.mp4 |
9.76MB |
| 03_checking-gradient-descent-for-convergence.en.srt |
8.32KB |
| 03_checking-gradient-descent-for-convergence.en.txt |
4.33KB |
| 03_checking-gradient-descent-for-convergence.mp4 |
10.99MB |
| 03_collaborative-filtering-algorithm.en.srt |
18.78KB |
| 03_collaborative-filtering-algorithm.en.txt |
10.00KB |
| 03_collaborative-filtering-algorithm.mp4 |
31.03MB |
| 03_comparing-gradient-to-stochastic-gradient.en.srt |
5.68KB |
| 03_comparing-gradient-to-stochastic-gradient.en.txt |
3.54KB |
| 03_comparing-gradient-to-stochastic-gradient.mp4 |
11.10MB |
| 03_computing-coefficient-of-each-ensemble-component.en.srt |
6.08KB |
| 03_computing-coefficient-of-each-ensemble-component.en.txt |
3.70KB |
| 03_computing-coefficient-of-each-ensemble-component.mp4 |
12.10MB |
| 03_coordinate-descent-for-lasso-unnormalized-features.en.srt |
2.39KB |
| 03_coordinate-descent-for-lasso-unnormalized-features.en.txt |
1.51KB |
| 03_coordinate-descent-for-lasso-unnormalized-features.mp4 |
5.91MB |
| 03_cost-function-formula.en.srt |
11.91KB |
| 03_cost-function-formula.en.txt |
6.24KB |
| 03_cost-function-formula.mp4 |
16.73MB |
| 03_cost-function-with-regularization.en.srt |
11.32KB |
| 03_cost-function-with-regularization.en.txt |
7.15KB |
| 03_cost-function-with-regularization.mp4 |
17.10MB |
| 03_creating-a-nearest-neighbors-model-for-image-retrieval.en.srt |
1.71KB |
| 03_creating-a-nearest-neighbors-model-for-image-retrieval.en.txt |
1.02KB |
| 03_creating-a-nearest-neighbors-model-for-image-retrieval.mp4 |
4.74MB |
| 03_creating-evaluating-a-popularity-based-song-recommender.en.srt |
5.50KB |
| 03_creating-evaluating-a-popularity-based-song-recommender.en.txt |
3.26KB |
| 03_creating-evaluating-a-popularity-based-song-recommender.mp4 |
13.15MB |
| 03_creating-the-word-count-vector.en.srt |
2.20KB |
| 03_creating-the-word-count-vector.en.txt |
1.31KB |
| 03_creating-the-word-count-vector.mp4 |
6.19MB |
| 03_creating-variables-in-python.en.srt |
8.19KB |
| 03_creating-variables-in-python.en.txt |
4.81KB |
| 03_creating-variables-in-python.mp4 |
17.69MB |
| 03_decision-boundary.en.srt |
14.12KB |
| 03_decision-boundary.en.txt |
7.28KB |
| 03_decision-boundary.mp4 |
18.94MB |
| 03_demand-prediction.en.srt |
26.60KB |
| 03_demand-prediction.en.txt |
13.99KB |
| 03_demand-prediction.mp4 |
24.19MB |
| 03_demo-of-deep-learning-model-on-imagenet-data.en.srt |
4.25KB |
| 03_demo-of-deep-learning-model-on-imagenet-data.en.txt |
2.67KB |
| 03_demo-of-deep-learning-model-on-imagenet-data.mp4 |
7.87MB |
| 03_deploying-an-ml-service.en.srt |
5.29KB |
| 03_deploying-an-ml-service.en.txt |
3.34KB |
| 03_deploying-an-ml-service.mp4 |
15.11MB |
| 03_discussing-the-closed-form-solution.en.srt |
6.19KB |
| 03_discussing-the-closed-form-solution.en.srt |
5.25KB |
| 03_discussing-the-closed-form-solution.en.txt |
3.74KB |
| 03_discussing-the-closed-form-solution.en.txt |
3.30KB |
| 03_discussing-the-closed-form-solution.mp4 |
13.45MB |
| 03_discussing-the-closed-form-solution.mp4 |
10.85MB |
| 03_don-t-trust-last-coefficients.en.srt |
2.19KB |
| 03_don-t-trust-last-coefficients.en.txt |
1.37KB |
| 03_don-t-trust-last-coefficients.mp4 |
6.02MB |
| 03_download-the-notebook-and-follow-along_instructions.html |
1.42KB |
| 03 download-the-notebook-and-follow-along Overfitting Demo Ridge Lasso ipynb zip |
198.44KB |
| 03_ensemble-classifiers.en.srt |
7.69KB |
| 03_ensemble-classifiers.en.txt |
4.87KB |
| 03_ensemble-classifiers.mp4 |
17.52MB |
| 03_error-vs-amount-of-data.en.srt |
6.70KB |
| 03_error-vs-amount-of-data.en.txt |
3.96KB |
| 03_error-vs-amount-of-data.mp4 |
14.68MB |
| 03_establishing-a-baseline-level-of-performance.en.srt |
15.85KB |
| 03_establishing-a-baseline-level-of-performance.en.txt |
8.26KB |
| 03_establishing-a-baseline-level-of-performance.mp4 |
19.39MB |
| 03_estimating-cluster-parameters-from-known-cluster-assignments.en.srt |
9.21KB |
| 03_estimating-cluster-parameters-from-known-cluster-assignments.en.txt |
5.69KB |
| 03_estimating-cluster-parameters-from-known-cluster-assignments.mp4 |
18.82MB |
| 03_example-of-computing-derivative-for-logistic-regression.en.srt |
7.10KB |
| 03_example-of-computing-derivative-for-logistic-regression.en.txt |
4.26KB |
| 03_example-of-computing-derivative-for-logistic-regression.mp4 |
13.75MB |
| 03_exploring-word-counts.en.srt |
7.20KB |
| 03_exploring-word-counts.en.txt |
4.28KB |
| 03_exploring-word-counts.mp4 |
18.75MB |
| 03_false-positives-false-negatives-and-confusion-matrices.en.srt |
8.47KB |
| 03_false-positives-false-negatives-and-confusion-matrices.en.txt |
5.16KB |
| 03_false-positives-false-negatives-and-confusion-matrices.mp4 |
16.92MB |
| 03_finding-related-items.en.srt |
10.21KB |
| 03_finding-related-items.en.txt |
5.40KB |
| 03_finding-related-items.mp4 |
16.62MB |
| 03_gradient-descent-intuition.en.srt |
9.99KB |
| 03_gradient-descent-intuition.en.txt |
5.28KB |
| 03_gradient-descent-intuition.mp4 |
13.20MB |
| 03_how-ridge-regression-balances-bias-and-variance.en.srt |
1.99KB |
| 03_how-ridge-regression-balances-bias-and-variance.en.txt |
1.26KB |
| 03_how-ridge-regression-balances-bias-and-variance.mp4 |
5.33MB |
| 03_how-to-handle-the-intercept.en.srt |
7.61KB |
| 03_how-to-handle-the-intercept.en.txt |
4.69KB |
| 03_how-to-handle-the-intercept.mp4 |
18.60MB |
| 03_important-changes-from-previous-courses_instructions.html |
2.68KB |
| 03_inference-making-predictions-forward-propagation.en.srt |
6.96KB |
| 03_inference-making-predictions-forward-propagation.en.txt |
4.34KB |
| 03_inference-making-predictions-forward-propagation.mp4 |
12.55MB |
| 03_influence-of-high-leverage-points-removing-center-city.en.srt |
9.18KB |
| 03_influence-of-high-leverage-points-removing-center-city.en.txt |
5.70KB |
| 03_influence-of-high-leverage-points-removing-center-city.mp4 |
19.52MB |
| 03_intuition-behind-decision-trees.en.srt |
2.18KB |
| 03_intuition-behind-decision-trees.en.txt |
1.39KB |
| 03_intuition-behind-decision-trees.mp4 |
5.90MB |
| 03_intuition-behind-linear-classifiers.en.srt |
5.17KB |
| 03_intuition-behind-linear-classifiers.en.txt |
3.16KB |
| 03_intuition-behind-linear-classifiers.mp4 |
12.31MB |
| 03_intuition-behind-maximum-likelihood-estimation.en.srt |
5.79KB |
| 03_intuition-behind-maximum-likelihood-estimation.en.txt |
3.64KB |
| 03_intuition-behind-maximum-likelihood-estimation.mp4 |
11.32MB |
| 03_k-means-a-clustering-algorithm.en.srt |
4.91KB |
| 03_k-means-a-clustering-algorithm.en.txt |
3.02KB |
| 03_k-means-a-clustering-algorithm.mp4 |
9.48MB |
| 03_k-means-algorithm.en.srt |
14.25KB |
| 03_k-means-algorithm.en.txt |
7.38KB |
| 03_k-means-algorithm.mp4 |
19.76MB |
| 03_k-nn-for-classification.en.srt |
2.59KB |
| 03_k-nn-for-classification.en.txt |
1.60KB |
| 03_k-nn-for-classification.mp4 |
6.58MB |
| 03_larger-neural-network-example-optional.en.srt |
11.92KB |
| 03_larger-neural-network-example-optional.en.txt |
7.31KB |
| 03_larger-neural-network-example-optional.mp4 |
26.11MB |
| 03_learning-the-state-value-function.en.srt |
25.16KB |
| 03_learning-the-state-value-function.en.txt |
12.93KB |
| 03_learning-the-state-value-function.mp4 |
31.14MB |
| 03_logistic-regression-model.en.srt |
4.82KB |
| 03_logistic-regression-model.en.txt |
2.77KB |
| 03_logistic-regression-model.mp4 |
11.40MB |
| 03_mapreduce-execution-overview-and-combiners.en.srt |
6.64KB |
| 03_mapreduce-execution-overview-and-combiners.en.txt |
4.00KB |
| 03_mapreduce-execution-overview-and-combiners.mp4 |
14.42MB |
| 03_matrix-multiplication-rules.en.srt |
11.38KB |
| 03_matrix-multiplication-rules.en.txt |
7.08KB |
| 03_matrix-multiplication-rules.mp4 |
16.14MB |
| 03_maximizing-a-1d-function-a-worked-example.en.srt |
3.47KB |
| 03_maximizing-a-1d-function-a-worked-example.en.txt |
1.99KB |
| 03_maximizing-a-1d-function-a-worked-example.mp4 |
7.33MB |
| 03_model-selection-and-training-cross-validation-test-sets.en.srt |
21.28KB |
| 03_model-selection-and-training-cross-validation-test-sets.en.txt |
11.23KB |
| 03_model-selection-and-training-cross-validation-test-sets.mp4 |
29.62MB |
| 03_module-2-recap.en.srt |
4.73KB |
| 03_module-2-recap.en.txt |
3.04KB |
| 03_module-2-recap.mp4 |
10.54MB |
| 03_neural-network-with-softmax-output.en.srt |
9.07KB |
| 03_neural-network-with-softmax-output.en.txt |
5.76KB |
| 03_neural-network-with-softmax-output.mp4 |
15.03MB |
| 03_nn-search-with-kd-trees.en.srt |
8.47KB |
| 03_nn-search-with-kd-trees.en.txt |
4.85KB |
| 03_nn-search-with-kd-trees.mp4 |
16.25MB |
| 03 normalizing-co-occurrence-matrices-and-leveraging-purchase-histories en srt |
8.14KB |
| 03 normalizing-co-occurrence-matrices-and-leveraging-purchase-histories en txt |
5.06KB |
| 03_normalizing-co-occurrence-matrices-and-leveraging-purchase-histories.mp4 |
19.46MB |
| 03_normalizing-features.en.srt |
4.35KB |
| 03_normalizing-features.en.txt |
2.80KB |
| 03_normalizing-features.mp4 |
9.35MB |
| 03 optional-another-perspecting-on-overfitting-in-logistic-regression en srt |
10.98KB |
| 03 optional-another-perspecting-on-overfitting-in-logistic-regression en txt |
6.81KB |
| 03_optional-another-perspecting-on-overfitting-in-logistic-regression.mp4 |
21.68MB |
| 03 optional-reading-worked-out-example-for-closed-form-solution instructions html |
12.45KB |
| 03_optional-tree-pruning-algorithm.en.srt |
4.69KB |
| 03_optional-tree-pruning-algorithm.en.txt |
3.01KB |
| 03_optional-tree-pruning-algorithm.mp4 |
11.89MB |
| 03_outline-of-second-half-of-course.en.srt |
9.13KB |
| 03_outline-of-second-half-of-course.en.txt |
5.71KB |
| 03_outline-of-second-half-of-course.mp4 |
20.16MB |
| 03_overfitting-in-decision-trees.en.srt |
7.34KB |
| 03_overfitting-in-decision-trees.en.txt |
4.49KB |
| 03_overfitting-in-decision-trees.mp4 |
16.82MB |
| 03_pca-in-code-optional.en.srt |
16.73KB |
| 03_pca-in-code-optional.en.txt |
8.64KB |
| 03_pca-in-code-optional.mp4 |
17.80MB |
| 03_polynomial-regression.en.srt |
5.23KB |
| 03_polynomial-regression.en.txt |
3.35KB |
| 03_polynomial-regression.mp4 |
11.56MB |
| 03_precision-recall-curve.en.srt |
7.82KB |
| 03_precision-recall-curve.en.txt |
4.84KB |
| 03_precision-recall-curve.mp4 |
15.66MB |
| 03_precision-recall-curves.en.srt |
8.21KB |
| 03_precision-recall-curves.en.txt |
5.16KB |
| 03_precision-recall-curves.mp4 |
16.28MB |
| 03_predictions-in-matrix-form.en.srt |
3.82KB |
| 03_predictions-in-matrix-form.en.txt |
2.30KB |
| 03_predictions-in-matrix-form.mp4 |
7.45MB |
| 03_putting-it-together.en.srt |
14.59KB |
| 03_putting-it-together.en.txt |
7.76KB |
| 03_putting-it-together.mp4 |
18.41MB |
| 03_random-forest-algorithm.en.srt |
8.75KB |
| 03_random-forest-algorithm.en.txt |
5.57KB |
| 03_random-forest-algorithm.mp4 |
12.72MB |
| 03_recommending-from-a-large-catalogue.en.srt |
10.20KB |
| 03_recommending-from-a-large-catalogue.en.txt |
6.37KB |
| 03_recommending-from-a-large-catalogue.mp4 |
17.98MB |
| 03_regression-fundamentals-data-model.en.srt |
9.74KB |
| 03_regression-fundamentals-data-model.en.txt |
5.95KB |
| 03_regression-fundamentals-data-model.mp4 |
22.35MB |
| 03_regression-with-features-of-multiple-inputs.en.srt |
4.92KB |
| 03_regression-with-features-of-multiple-inputs.en.txt |
3.07KB |
| 03_regression-with-features-of-multiple-inputs.mp4 |
12.60MB |
| 03_relationship-to-k-means.en.srt |
4.13KB |
| 03_relationship-to-k-means.en.txt |
2.61KB |
| 03_relationship-to-k-means.mp4 |
10.37MB |
| 03_review-of-basics-of-conditional-probabilities.en.srt |
9.18KB |
| 03_review-of-basics-of-conditional-probabilities.en.txt |
5.56KB |
| 03_review-of-basics-of-conditional-probabilities.mp4 |
19.91MB |
| 03_review-of-overfitting-in-regression.en.srt |
4.72KB |
| 03_review-of-overfitting-in-regression.en.txt |
2.97KB |
| 03_review-of-overfitting-in-regression.mp4 |
9.60MB |
| 03_rewriting-the-model-for-all-observations-in-matrix-notation.en.srt |
4.82KB |
| 03_rewriting-the-model-for-all-observations-in-matrix-notation.en.txt |
2.94KB |
| 03_rewriting-the-model-for-all-observations-in-matrix-notation.mp4 |
10.19MB |
| 03_rule-of-thumb-for-choosing-step-size.en.srt |
4.30KB |
| 03_rule-of-thumb-for-choosing-step-size.en.txt |
2.59KB |
| 03_rule-of-thumb-for-choosing-step-size.mp4 |
9.36MB |
| 03_scaling-mixtures-of-gaussians-for-document-clustering.en.srt |
7.04KB |
| 03_scaling-mixtures-of-gaussians-for-document-clustering.en.txt |
4.56KB |
| 03_scaling-mixtures-of-gaussians-for-document-clustering.mp4 |
17.21MB |
| 03_selecting-best-feature-to-split-on.en.srt |
8.47KB |
| 03_selecting-best-feature-to-split-on.en.txt |
5.12KB |
| 03_selecting-best-feature-to-split-on.mp4 |
15.14MB |
| 03_smart-initialization-via-k-means.en.srt |
5.26KB |
| 03_smart-initialization-via-k-means.en.txt |
3.25KB |
| 03_smart-initialization-via-k-means.mp4 |
12.09MB |
| 03_splitting-the-data-into-training-and-test-sets.en.srt |
2.90KB |
| 03_splitting-the-data-into-training-and-test-sets.en.txt |
1.75KB |
| 03_splitting-the-data-into-training-and-test-sets.mp4 |
6.17MB |
| 03_strategy-1-purification-by-skipping-missing-data.en.srt |
5.31KB |
| 03_strategy-1-purification-by-skipping-missing-data.en.txt |
3.33KB |
| 03_strategy-1-purification-by-skipping-missing-data.mp4 |
12.92MB |
| 03_supervised-learning-part-2.en.srt |
8.85KB |
| 03_supervised-learning-part-2.en.txt |
5.51KB |
| 03_supervised-learning-part-2.mp4 |
14.39MB |
| 03_test-error-what-we-can-actually-compute.en.srt |
5.91KB |
| 03_test-error-what-we-can-actually-compute.en.txt |
3.72KB |
| 03_test-error-what-we-can-actually-compute.mp4 |
12.93MB |
| 03_the-lasso-objective-and-its-coefficient-path.en.srt |
7.71KB |
| 03_the-lasso-objective-and-its-coefficient-path.en.txt |
4.70KB |
| 03_the-lasso-objective-and-its-coefficient-path.mp4 |
15.55MB |
| 03_the-return-in-reinforcement-learning.en.srt |
15.66KB |
| 03_the-return-in-reinforcement-learning.en.txt |
8.18KB |
| 03_the-return-in-reinforcement-learning.mp4 |
29.01MB |
| 03_timeline-of-scalable-machine-learning-stochastic-gradient.en.srt |
6.28KB |
| 03_timeline-of-scalable-machine-learning-stochastic-gradient.en.txt |
3.96KB |
| 03_timeline-of-scalable-machine-learning-stochastic-gradient.mp4 |
12.07MB |
| 03_training-evaluating-a-classifier-using-raw-image-pixels.en.srt |
6.79KB |
| 03_training-evaluating-a-classifier-using-raw-image-pixels.en.txt |
3.88KB |
| 03_training-evaluating-a-classifier-using-raw-image-pixels.mp4 |
14.98MB |
| 03_using-random-lines-to-partition-points.en.srt |
7.82KB |
| 03_using-random-lines-to-partition-points.en.txt |
4.98KB |
| 03_using-random-lines-to-partition-points.mp4 |
17.06MB |
| 03_using-the-fitted-line.en.srt |
6.72KB |
| 03_using-the-fitted-line.en.txt |
3.94KB |
| 03_using-the-fitted-line.mp4 |
16.62MB |
| 03_vectorization-part-2.en.srt |
10.05KB |
| 03_vectorization-part-2.en.txt |
5.27KB |
| 03_vectorization-part-2.mp4 |
17.26MB |
| 03_very-optional-deriving-probability-y-1-given-x.en.srt |
2.11KB |
| 03_very-optional-deriving-probability-y-1-given-x.en.txt |
1.19KB |
| 03_very-optional-deriving-probability-y-1-given-x.mp4 |
4.96MB |
| 03_visualizing-decision-boundaries.en.srt |
7.35KB |
| 03_visualizing-decision-boundaries.en.txt |
4.60KB |
| 03_visualizing-decision-boundaries.mp4 |
13.20MB |
| 03_visualizing-effect-of-l2-regularization-in-logistic-regression.en.srt |
6.80KB |
| 03_visualizing-effect-of-l2-regularization-in-logistic-regression.en.txt |
4.35KB |
| 03_visualizing-effect-of-l2-regularization-in-logistic-regression.mp4 |
13.56MB |
| 03_visualizing-the-lasso-cost-and-solution.en.srt |
8.13KB |
| 03_visualizing-the-lasso-cost-and-solution.en.txt |
4.85KB |
| 03_visualizing-the-lasso-cost-and-solution.mp4 |
18.40MB |
| 03_weighted-k-nearest-neighbors.en.srt |
5.59KB |
| 03_weighted-k-nearest-neighbors.en.txt |
3.57KB |
| 03_weighted-k-nearest-neighbors.mp4 |
13.81MB |
| 03_welcome.en.srt |
2.87KB |
| 03_welcome.en.txt |
1.82KB |
| 03_welcome.mp4 |
4.87MB |
| 03_welcome-and-introduction-to-clustering-and-retrieval-tasks.en.srt |
9.88KB |
| 03_welcome-and-introduction-to-clustering-and-retrieval-tasks.en.txt |
6.19KB |
| 03_welcome-and-introduction-to-clustering-and-retrieval-tasks.mp4 |
19.84MB |
| 03 welcome-to-the-classification-course-a-part-of-the-machine-learning en srt |
1.74KB |
| 03 welcome-to-the-classification-course-a-part-of-the-machine-learning en txt |
1.12KB |
| 03_welcome-to-the-classification-course-a-part-of-the-machine-learning.mp4 |
4.62MB |
| 03_welcome-to-this-course-and-specialization.en.srt |
1.18KB |
| 03_welcome-to-this-course-and-specialization.en.txt |
637B |
| 03_welcome-to-this-course-and-specialization.mp4 |
3.32MB |
| 03_what-do-we-mean-by-loss.en.srt |
6.19KB |
| 03_what-do-we-mean-by-loss.en.txt |
3.84KB |
| 03_what-do-we-mean-by-loss.mp4 |
13.85MB |
| 03_what-is-an-intelligent-restaurant-review-system.en.srt |
6.27KB |
| 03_what-is-an-intelligent-restaurant-review-system.en.txt |
3.88KB |
| 03_what-is-an-intelligent-restaurant-review-system.mp4 |
18.51MB |
| 03_what-is-a-visual-product-recommender.en.srt |
5.67KB |
| 03_what-is-a-visual-product-recommender.en.txt |
3.46KB |
| 03_what-is-a-visual-product-recommender.mp4 |
16.65MB |
| 03_what-is-good-performance-for-a-classifier.en.srt |
5.45KB |
| 03_what-is-good-performance-for-a-classifier.en.txt |
3.37KB |
| 03_what-is-good-performance-for-a-classifier.mp4 |
13.81MB |
| 03_what-is-the-document-retrieval-task.en.srt |
2.15KB |
| 03_what-is-the-document-retrieval-task.en.txt |
1.33KB |
| 03_what-is-the-document-retrieval-task.mp4 |
6.35MB |
| 03_what-is-the-goal-and-how-might-you-naively-address-it.en.srt |
4.94KB |
| 03_what-is-the-goal-and-how-might-you-naively-address-it.en.txt |
3.09KB |
| 03_what-is-the-goal-and-how-might-you-naively-address-it.mp4 |
10.06MB |
| 03_whats-ahead-in-the-specialization.en.srt |
8.24KB |
| 03_whats-ahead-in-the-specialization.en.txt |
5.23KB |
| 03_whats-ahead-in-the-specialization.mp4 |
22.19MB |
| 03_what-you-ll-be-able-to-do.en.srt |
1.48KB |
| 03_what-you-ll-be-able-to-do.en.txt |
918B |
| 03_what-you-ll-be-able-to-do.mp4 |
4.45MB |
| 03_where-we-see-recommender-systems-in-action.en.srt |
9.74KB |
| 03_where-we-see-recommender-systems-in-action.en.txt |
6.16KB |
| 03_where-we-see-recommender-systems-in-action.mp4 |
26.70MB |
| 03_why-do-we-need-activation-functions.en.srt |
7.63KB |
| 03_why-do-we-need-activation-functions.en.txt |
4.01KB |
| 03_why-do-we-need-activation-functions.mp4 |
12.93MB |
| 03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.srt |
4.40KB |
| 03_writing-scaled-euclidean-distance-using-weighted-inner-products.en.txt |
2.58KB |
| 03_writing-scaled-euclidean-distance-using-weighted-inner-products.mp4 |
8.82MB |
| 04_a-brief-recap.en.srt |
2.32KB |
| 04_a-brief-recap.en.srt |
2.05KB |
| 04_a-brief-recap.en.txt |
1.43KB |
| 04_a-brief-recap.en.txt |
1.26KB |
| 04_a-brief-recap.mp4 |
5.24MB |
| 04_a-brief-recap.mp4 |
4.70MB |
| 04_algorithm-refinement-improved-neural-network-architecture.en.srt |
4.71KB |
| 04_algorithm-refinement-improved-neural-network-architecture.en.txt |
2.46KB |
| 04_algorithm-refinement-improved-neural-network-architecture.mp4 |
7.79MB |
| 04_approach-2-gradient-descent.en.srt |
10.05KB |
| 04_approach-2-gradient-descent.en.srt |
6.93KB |
| 04_approach-2-gradient-descent.en.srt |
2.08KB |
| 04_approach-2-gradient-descent.en.txt |
5.99KB |
| 04_approach-2-gradient-descent.en.txt |
3.88KB |
| 04_approach-2-gradient-descent.en.txt |
1.26KB |
| 04_approach-2-gradient-descent.mp4 |
22.08MB |
| 04_approach-2-gradient-descent.mp4 |
18.36MB |
| 04_approach-2-gradient-descent.mp4 |
5.36MB |
| 04_assessing-the-quality-and-choosing-the-number-of-clusters.en.srt |
11.04KB |
| 04_assessing-the-quality-and-choosing-the-number-of-clusters.en.txt |
6.74KB |
| 04_assessing-the-quality-and-choosing-the-number-of-clusters.mp4 |
21.18MB |
| 04_assumed-background.en.srt |
5.45KB |
| 04_assumed-background.en.txt |
3.40KB |
| 04_assumed-background.mp4 |
12.02MB |
| 04_binary-labels-favs-likes-and-clicks.en.srt |
10.61KB |
| 04_binary-labels-favs-likes-and-clicks.en.txt |
6.68KB |
| 04_binary-labels-favs-likes-and-clicks.mp4 |
19.84MB |
| 04_boosting.en.srt |
9.12KB |
| 04_boosting.en.txt |
5.77KB |
| 04_boosting.mp4 |
21.42MB |
| 04_building-a-recommender-system-via-classification.en.srt |
6.02KB |
| 04_building-a-recommender-system-via-classification.en.txt |
3.87KB |
| 04_building-a-recommender-system-via-classification.mp4 |
13.42MB |
| 04_choosing-the-learning-rate.en.srt |
9.60KB |
| 04_choosing-the-learning-rate.en.txt |
5.05KB |
| 04_choosing-the-learning-rate.mp4 |
16.31MB |
| 04_complexity-of-all-subsets.en.srt |
4.01KB |
| 04_complexity-of-all-subsets.en.txt |
2.46KB |
| 04_complexity-of-all-subsets.mp4 |
6.84MB |
| 04_complexity-of-nn-search-with-kd-trees.en.srt |
6.46KB |
| 04_complexity-of-nn-search-with-kd-trees.en.txt |
4.01KB |
| 04_complexity-of-nn-search-with-kd-trees.mp4 |
13.51MB |
| 04_components-of-latent-dirichlet-allocation-model.en.srt |
3.28KB |
| 04_components-of-latent-dirichlet-allocation-model.en.txt |
2.00KB |
| 04_components-of-latent-dirichlet-allocation-model.mp4 |
10.60MB |
| 04_computing-exploring-tf-idfs.en.srt |
8.28KB |
| 04_computing-exploring-tf-idfs.en.txt |
4.86KB |
| 04_computing-exploring-tf-idfs.mp4 |
18.35MB |
| 04_computing-the-cost-of-a-d-dimensional-curve.en.srt |
10.11KB |
| 04_computing-the-cost-of-a-d-dimensional-curve.en.txt |
6.05KB |
| 04_computing-the-cost-of-a-d-dimensional-curve.mp4 |
21.24MB |
| 04_conditional-statements-and-loops-in-python.en.srt |
9.42KB |
| 04_conditional-statements-and-loops-in-python.en.txt |
5.29KB |
| 04_conditional-statements-and-loops-in-python.mp4 |
20.14MB |
| 04 coordinate-descent-for-least-squares-regression-normalized-features en srt |
9.80KB |
| 04 coordinate-descent-for-least-squares-regression-normalized-features en txt |
5.94KB |
| 04_coordinate-descent-for-least-squares-regression-normalized-features.mp4 |
20.81MB |
| 04_cost-function-intuition.en.srt |
20.20KB |
| 04_cost-function-intuition.en.txt |
10.16KB |
| 04_cost-function-intuition.mp4 |
29.56MB |
| 04_course-overview.en.srt |
5.26KB |
| 04_course-overview.en.txt |
3.34KB |
| 04_course-overview.mp4 |
10.58MB |
| 04_creating-evaluating-a-personalized-song-recommender.en.srt |
6.36KB |
| 04_creating-evaluating-a-personalized-song-recommender.en.txt |
3.75KB |
| 04_creating-evaluating-a-personalized-song-recommender.mp4 |
16.40MB |
| 04_data-likelihood.en.srt |
9.98KB |
| 04_data-likelihood.en.txt |
5.98KB |
| 04_data-likelihood.mp4 |
18.30MB |
| 04_decision-boundaries.en.srt |
4.49KB |
| 04_decision-boundaries.en.txt |
2.77KB |
| 04_decision-boundaries.mp4 |
11.10MB |
| 04_defining-more-bins.en.srt |
4.15KB |
| 04_defining-more-bins.en.txt |
2.63KB |
| 04_defining-more-bins.mp4 |
10.54MB |
| 04_defining-overfitting.en.srt |
2.59KB |
| 04_defining-overfitting.en.txt |
1.50KB |
| 04_defining-overfitting.mp4 |
6.10MB |
| 04_developing-and-evaluating-an-anomaly-detection-system.en.srt |
17.51KB |
| 04_developing-and-evaluating-an-anomaly-detection-system.en.txt |
9.24KB |
| 04_developing-and-evaluating-an-anomaly-detection-system.mp4 |
23.90MB |
| 04_discovering-hidden-structure-by-matrix-factorization.en.srt |
8.54KB |
| 04_discovering-hidden-structure-by-matrix-factorization.en.txt |
5.13KB |
| 04_discovering-hidden-structure-by-matrix-factorization.mp4 |
17.22MB |
| 04_distance-metrics-cosine-similarity.en.srt |
9.84KB |
| 04_distance-metrics-cosine-similarity.en.txt |
6.15KB |
| 04_distance-metrics-cosine-similarity.mp4 |
20.12MB |
| 04_download-the-notebook-and-follow-along_instructions.html |
1.62KB |
| 04_download-the-notebook-and-follow-along_instructions.html |
1.58KB |
| 04 download-the-notebook-and-follow-along Overfitting Demo Ridge Lasso ipynb zip |
198.44KB |
| 04 download-the-notebook-and-follow-along Overfitting Demo Ridge Lasso ipynb zip |
198.44KB |
| 04_effect-of-coefficient-values-on-predicted-probabilities.en.srt |
8.18KB |
| 04_effect-of-coefficient-values-on-predicted-probabilities.en.txt |
4.94KB |
| 04_effect-of-coefficient-values-on-predicted-probabilities.mp4 |
16.99MB |
| 04_estimating-cluster-parameters-from-soft-assignments.en.srt |
9.50KB |
| 04_estimating-cluster-parameters-from-soft-assignments.en.txt |
6.08KB |
| 04_estimating-cluster-parameters-from-soft-assignments.mp4 |
21.40MB |
| 04_ethical-use-of-recommender-systems.en.srt |
16.92KB |
| 04_ethical-use-of-recommender-systems.en.txt |
8.96KB |
| 04_ethical-use-of-recommender-systems.mp4 |
24.83MB |
| 04_example-recognizing-images.en.srt |
10.18KB |
| 04_example-recognizing-images.en.txt |
5.33KB |
| 04_example-recognizing-images.mp4 |
14.59MB |
| 04_examples-of-classification-tasks.en.srt |
7.23KB |
| 04_examples-of-classification-tasks.en.txt |
4.48KB |
| 04_examples-of-classification-tasks.mp4 |
23.02MB |
| 04_exploring-the-most-popular-product.en.srt |
5.53KB |
| 04_exploring-the-most-popular-product.en.txt |
3.32KB |
| 04_exploring-the-most-popular-product.mp4 |
12.11MB |
| 04_feature-selection-lasso-and-nearest-neighbor-regression.en.srt |
5.97KB |
| 04_feature-selection-lasso-and-nearest-neighbor-regression.en.txt |
3.92KB |
| 04_feature-selection-lasso-and-nearest-neighbor-regression.mp4 |
12.06MB |
| 04_finding-the-max-via-hill-climbing.en.srt |
7.87KB |
| 04_finding-the-max-via-hill-climbing.en.txt |
4.63KB |
| 04_finding-the-max-via-hill-climbing.mp4 |
15.69MB |
| 04_gradient-descent-for-multiple-linear-regression.en.srt |
11.09KB |
| 04_gradient-descent-for-multiple-linear-regression.en.txt |
5.90KB |
| 04_gradient-descent-for-multiple-linear-regression.mp4 |
19.36MB |
| 04_hope-for-unsupervised-learning-and-some-challenge-cases.en.srt |
5.41KB |
| 04_hope-for-unsupervised-learning-and-some-challenge-cases.en.txt |
3.43KB |
| 04_hope-for-unsupervised-learning-and-some-challenge-cases.mp4 |
11.58MB |
| 04_improved-implementation-of-softmax.en.srt |
13.39KB |
| 04_improved-implementation-of-softmax.en.txt |
7.07KB |
| 04_improved-implementation-of-softmax.mp4 |
15.05MB |
| 04_influence-of-high-leverage-points-removing-high-end-towns.en.srt |
3.92KB |
| 04_influence-of-high-leverage-points-removing-high-end-towns.en.txt |
2.50KB |
| 04_influence-of-high-leverage-points-removing-high-end-towns.mp4 |
9.33MB |
| 04_interacting-with-columns-of-an-sframe.en.srt |
5.21KB |
| 04_interacting-with-columns-of-an-sframe.en.txt |
2.93KB |
| 04_interacting-with-columns-of-an-sframe.mp4 |
10.46MB |
| 04_interpreting-derivative-for-logistic-regression.en.srt |
6.53KB |
| 04_interpreting-derivative-for-logistic-regression.en.txt |
3.95KB |
| 04_interpreting-derivative-for-logistic-regression.mp4 |
13.67MB |
| 04_interpreting-the-fitted-line.en.srt |
6.51KB |
| 04_interpreting-the-fitted-line.en.txt |
3.90KB |
| 04_interpreting-the-fitted-line.mp4 |
15.17MB |
| 04_interpreting-the-multiple-regression-fit.en.srt |
10.06KB |
| 04_interpreting-the-multiple-regression-fit.en.txt |
6.41KB |
| 04_interpreting-the-multiple-regression-fit.mp4 |
24.67MB |
| 04_k-nn-algorithm.en.srt |
8.13KB |
| 04_k-nn-algorithm.en.txt |
5.12KB |
| 04_k-nn-algorithm.mp4 |
17.38MB |
| 04 learning-a-simple-regression-model-to-predict-house-prices-from-house-size en srt |
4.32KB |
| 04 learning-a-simple-regression-model-to-predict-house-prices-from-house-size en txt |
2.57KB |
| 04 learning-a-simple-regression-model-to-predict-house-prices-from-house-size mp4 |
8.47MB |
| 04_learning-curves.en.srt |
20.05KB |
| 04_learning-curves.en.srt |
7.12KB |
| 04_learning-curves.en.txt |
10.49KB |
| 04_learning-curves.en.txt |
4.38KB |
| 04_learning-curves.mp4 |
23.28MB |
| 04_learning-curves.mp4 |
22.21MB |
| 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.en.srt |
9.29KB |
| 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.en.txt |
5.71KB |
| 04_learning-l2-regularized-logistic-regression-with-gradient-ascent.mp4 |
19.08MB |
| 04_learning-rate.en.srt |
11.21KB |
| 04_learning-rate.en.txt |
6.88KB |
| 04_learning-rate.mp4 |
16.94MB |
| 04_learning-very-non-linear-features-with-neural-networks.en.srt |
13.59KB |
| 04_learning-very-non-linear-features-with-neural-networks.en.txt |
8.36KB |
| 04_learning-very-non-linear-features-with-neural-networks.mp4 |
32.85MB |
| 04_linear-regression-a-model-based-approach.en.srt |
6.87KB |
| 04_linear-regression-a-model-based-approach.en.txt |
4.09KB |
| 04_linear-regression-a-model-based-approach.mp4 |
12.72MB |
| 04_making-decisions-policies-in-reinforcement-learning.en.srt |
3.74KB |
| 04_making-decisions-policies-in-reinforcement-learning.en.txt |
1.99KB |
| 04_making-decisions-policies-in-reinforcement-learning.mp4 |
5.81MB |
| 04_mapreduce-for-k-means.en.srt |
8.04KB |
| 04_mapreduce-for-k-means.en.txt |
4.80KB |
| 04_mapreduce-for-k-means.mp4 |
18.54MB |
| 04_matrix-multiplication-code.en.srt |
8.82KB |
| 04_matrix-multiplication-code.en.txt |
4.58KB |
| 04_matrix-multiplication-code.mp4 |
13.38MB |
| 04_modeling-seasonality.en.srt |
10.61KB |
| 04_modeling-seasonality.en.txt |
6.75KB |
| 04_modeling-seasonality.mp4 |
30.01MB |
| 04_module-3-recap.en.srt |
8.59KB |
| 04_module-3-recap.en.txt |
5.64KB |
| 04_module-3-recap.mp4 |
19.72MB |
| 04_optimization-objective.en.srt |
13.67KB |
| 04_optimization-objective.en.txt |
8.62KB |
| 04_optimization-objective.mp4 |
29.51MB |
| 04_optional-a-worked-out-example-for-em_instructions.html |
210.84KB |
| 04_optional-learning-from-batches-of-data.en.srt |
5.00KB |
| 04_optional-learning-from-batches-of-data.en.txt |
3.08KB |
| 04_optional-learning-from-batches-of-data.mp4 |
11.99MB |
| 04_other-examples-of-clustering.en.srt |
7.50KB |
| 04_other-examples-of-clustering.en.txt |
4.79KB |
| 04_other-examples-of-clustering.mp4 |
19.77MB |
| 04_other-examples-of-deep-learning-in-computer-vision.en.srt |
2.02KB |
| 04_other-examples-of-deep-learning-in-computer-vision.en.txt |
1.20KB |
| 04_other-examples-of-deep-learning-in-computer-vision.mp4 |
6.71MB |
| 04_other-regression-examples.en.srt |
5.01KB |
| 04_other-regression-examples.en.txt |
3.23KB |
| 04_other-regression-examples.mp4 |
13.48MB |
| 04_overfitting-demo.en.srt |
8.52KB |
| 04_overfitting-demo.en.txt |
5.34KB |
| 04_overfitting-demo.mp4 |
16.94MB |
| 04_overfitting-in-classification.en.srt |
6.48KB |
| 04_overfitting-in-classification.en.txt |
4.03KB |
| 04_overfitting-in-classification.mp4 |
14.24MB |
| 04_querying-the-nearest-neighbors-model-to-retrieve-images.en.srt |
5.83KB |
| 04_querying-the-nearest-neighbors-model-to-retrieve-images.en.txt |
3.28KB |
| 04_querying-the-nearest-neighbors-model-to-retrieve-images.mp4 |
13.30MB |
| 04_random-stochastic-environment-optional.en.srt |
13.10KB |
| 04_random-stochastic-environment-optional.en.txt |
6.92KB |
| 04_random-stochastic-environment-optional.mp4 |
19.27MB |
| 04_regression-fundamentals-the-task.en.srt |
3.13KB |
| 04_regression-fundamentals-the-task.en.txt |
1.91KB |
| 04_regression-fundamentals-the-task.mp4 |
7.33MB |
| 04_regularized-linear-regression.en.srt |
12.03KB |
| 04_regularized-linear-regression.en.txt |
6.36KB |
| 04_regularized-linear-regression.mp4 |
19.81MB |
| 04_reweighing-data-to-focus-on-mistakes.en.srt |
6.41KB |
| 04_reweighing-data-to-focus-on-mistakes.en.txt |
4.07KB |
| 04_reweighing-data-to-focus-on-mistakes.mp4 |
11.78MB |
| 04_strategy-2-purification-by-imputing-missing-data.en.srt |
6.62KB |
| 04_strategy-2-purification-by-imputing-missing-data.en.txt |
4.11KB |
| 04_strategy-2-purification-by-imputing-missing-data.mp4 |
15.88MB |
| 04_task-of-learning-decision-trees-from-data.en.srt |
4.68KB |
| 04_task-of-learning-decision-trees-from-data.en.txt |
2.93KB |
| 04_task-of-learning-decision-trees-from-data.mp4 |
10.97MB |
| 04_thank-you.en.srt |
2.31KB |
| 04_thank-you.en.txt |
1.39KB |
| 04_thank-you.mp4 |
8.23MB |
| 04_the-capstone-and-an-example-intelligent-application.en.srt |
10.10KB |
| 04_the-capstone-and-an-example-intelligent-application.en.txt |
6.21KB |
| 04_the-capstone-and-an-example-intelligent-application.mp4 |
22.15MB |
| 04_the-dendrogram.en.srt |
6.97KB |
| 04_the-dendrogram.en.txt |
4.45KB |
| 04_the-dendrogram.mp4 |
15.35MB |
| 04_training-evaluating-a-classifier-using-deep-features.en.srt |
9.25KB |
| 04_training-evaluating-a-classifier-using-deep-features.en.txt |
5.36KB |
| 04_training-evaluating-a-classifier-using-deep-features.mp4 |
17.86MB |
| 04_transfer-learning-using-data-from-a-different-task.en.srt |
20.17KB |
| 04_transfer-learning-using-data-from-a-different-task.en.txt |
10.54KB |
| 04_transfer-learning-using-data-from-a-different-task.mp4 |
19.02MB |
| 04_univariate-gaussian-distributions.en.srt |
3.77KB |
| 04_univariate-gaussian-distributions.en.txt |
2.42KB |
| 04_univariate-gaussian-distributions.mp4 |
9.02MB |
| 04_unsupervised-learning-part-1.en.srt |
11.58KB |
| 04_unsupervised-learning-part-1.en.txt |
7.35KB |
| 04_unsupervised-learning-part-1.mp4 |
18.72MB |
| 04_using-one-hot-encoding-of-categorical-features.en.srt |
6.60KB |
| 04_using-one-hot-encoding-of-categorical-features.en.txt |
4.32KB |
| 04_using-one-hot-encoding-of-categorical-features.mp4 |
14.17MB |
| 04_using-probabilities-in-classification.en.srt |
3.32KB |
| 04_using-probabilities-in-classification.en.txt |
2.10KB |
| 04_using-probabilities-in-classification.mp4 |
9.90MB |
| 04_using-the-output-of-collapsed-gibbs-sampling.en.srt |
5.64KB |
| 04_using-the-output-of-collapsed-gibbs-sampling.en.txt |
3.55KB |
| 04_using-the-output-of-collapsed-gibbs-sampling.mp4 |
15.23MB |
| 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.en.srt |
8.06KB |
| 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.en.txt |
4.71KB |
| 04_very-optional-rewriting-the-log-likelihood-into-a-simpler-form.mp4 |
17.99MB |
| 04_what-happens-after-deployment.en.srt |
11.64KB |
| 04_what-happens-after-deployment.en.txt |
7.30KB |
| 04_what-happens-after-deployment.mp4 |
29.95MB |
| 04_what-is-the-course-about.en.srt |
5.43KB |
| 04_what-is-the-course-about.en.txt |
3.42KB |
| 04_what-is-the-course-about.mp4 |
11.19MB |
| 04_what-is-this-course-about.en.srt |
9.38KB |
| 04_what-is-this-course-about.en.txt |
5.83KB |
| 04_what-is-this-course-about.mp4 |
22.06MB |
| 04_when-to-stop-recursing.en.srt |
6.16KB |
| 04_when-to-stop-recursing.en.txt |
3.94KB |
| 04_when-to-stop-recursing.mp4 |
15.50MB |
| 04_who-we-are.en.srt |
8.78KB |
| 04_who-we-are.en.txt |
5.16KB |
| 04_who-we-are.mp4 |
28.43MB |
| 04_word-count-representation-for-measuring-similarity.en.srt |
9.64KB |
| 04_word-count-representation-for-measuring-similarity.en.txt |
5.99KB |
| 04_word-count-representation-for-measuring-similarity.mp4 |
22.28MB |
| 04_xgboost.en.srt |
9.42KB |
| 04_xgboost.en.txt |
6.04KB |
| 04_xgboost.mp4 |
21.08MB |
| 05_adding-higher-order-effects.en.srt |
6.07KB |
| 05_adding-higher-order-effects.en.txt |
3.55KB |
| 05_adding-higher-order-effects.mp4 |
10.41MB |
| 05_agglomerative-clustering-details.en.srt |
10.05KB |
| 05_agglomerative-clustering-details.en.txt |
6.53KB |
| 05_agglomerative-clustering-details.mp4 |
20.18MB |
| 05_algorithm-refinement-greedy-policy.en.srt |
14.24KB |
| 05_algorithm-refinement-greedy-policy.en.txt |
7.54KB |
| 05_algorithm-refinement-greedy-policy.mp4 |
25.27MB |
| 05_anomaly-detection-vs-supervised-learning.en.srt |
11.16KB |
| 05_anomaly-detection-vs-supervised-learning.en.txt |
7.13KB |
| 05_anomaly-detection-vs-supervised-learning.mp4 |
20.31MB |
| 05_asymmetric-cost-functions.en.srt |
4.36KB |
| 05_asymmetric-cost-functions.en.txt |
2.65KB |
| 05_asymmetric-cost-functions.mp4 |
9.40MB |
| 05_bivariate-and-multivariate-gaussians.en.srt |
9.04KB |
| 05_bivariate-and-multivariate-gaussians.en.txt |
5.69KB |
| 05_bivariate-and-multivariate-gaussians.mp4 |
21.26MB |
| 05_bringing-it-all-together-featurized-matrix-factorization.en.srt |
4.15KB |
| 05_bringing-it-all-together-featurized-matrix-factorization.en.txt |
2.63KB |
| 05_bringing-it-all-together-featurized-matrix-factorization.mp4 |
11.26MB |
| 05_challenges-of-deep-learning.en.srt |
3.33KB |
| 05_challenges-of-deep-learning.en.txt |
2.11KB |
| 05_challenges-of-deep-learning.mp4 |
11.07MB |
| 05_classification-with-multiple-outputs-optional.en.srt |
6.76KB |
| 05_classification-with-multiple-outputs-optional.en.txt |
3.58KB |
| 05_classification-with-multiple-outputs-optional.mp4 |
11.31MB |
| 05_class-probabilities.en.srt |
2.52KB |
| 05_class-probabilities.en.txt |
1.53KB |
| 05_class-probabilities.mp4 |
8.66MB |
| 05_computing-distances-between-wikipedia-articles.en.srt |
5.69KB |
| 05_computing-distances-between-wikipedia-articles.en.txt |
3.34KB |
| 05_computing-distances-between-wikipedia-articles.mp4 |
15.38MB |
| 05_continuous-valued-features.en.srt |
8.61KB |
| 05_continuous-valued-features.en.txt |
5.50KB |
| 05_continuous-valued-features.mp4 |
15.89MB |
| 05_creating-functions-and-lambdas-in-python.en.srt |
4.16KB |
| 05_creating-functions-and-lambdas-in-python.en.txt |
2.48KB |
| 05_creating-functions-and-lambdas-in-python.mp4 |
9.06MB |
| 05_deciding-what-to-try-next-revisited.en.srt |
14.51KB |
| 05_deciding-what-to-try-next-revisited.en.txt |
7.75KB |
| 05_deciding-what-to-try-next-revisited.mp4 |
28.02MB |
| 05_defining-which-reviews-have-positive-or-negative-sentiment.en.srt |
5.01KB |
| 05_defining-which-reviews-have-positive-or-negative-sentiment.en.txt |
2.90KB |
| 05_defining-which-reviews-have-positive-or-negative-sentiment.mp4 |
11.62MB |
| 05_evaluating-error-rmse-of-the-simple-model.en.srt |
2.59KB |
| 05_evaluating-error-rmse-of-the-simple-model.en.txt |
1.47KB |
| 05_evaluating-error-rmse-of-the-simple-model.mp4 |
6.15MB |
| 05_feature-by-feature-update.en.srt |
8.74KB |
| 05_feature-by-feature-update.en.txt |
5.05KB |
| 05_feature-by-feature-update.mp4 |
18.98MB |
| 05_feature-engineering.en.srt |
4.86KB |
| 05_feature-engineering.en.txt |
2.54KB |
| 05_feature-engineering.mp4 |
7.85MB |
| 05_finding-best-linear-classifier-with-gradient-ascent.en.srt |
4.26KB |
| 05_finding-best-linear-classifier-with-gradient-ascent.en.txt |
2.74KB |
| 05_finding-best-linear-classifier-with-gradient-ascent.mp4 |
9.94MB |
| 05_finding-the-min-via-hill-descent.en.srt |
3.70KB |
| 05_finding-the-min-via-hill-descent.en.txt |
2.21KB |
| 05_finding-the-min-via-hill-descent.mp4 |
8.54MB |
| 05_full-cycle-of-a-machine-learning-project.en.srt |
14.53KB |
| 05_full-cycle-of-a-machine-learning-project.en.txt |
7.70KB |
| 05_full-cycle-of-a-machine-learning-project.mp4 |
16.35MB |
| 05_goal-of-lda-inference.en.srt |
6.72KB |
| 05_goal-of-lda-inference.en.txt |
4.37KB |
| 05_goal-of-lda-inference.mp4 |
18.22MB |
| 05_gradient-descent-for-linear-regression.en.srt |
9.06KB |
| 05_gradient-descent-for-linear-regression.en.txt |
4.89KB |
| 05_gradient-descent-for-linear-regression.mp4 |
16.38MB |
| 05_greedy-algorithms.en.srt |
9.94KB |
| 05_greedy-algorithms.en.txt |
6.11KB |
| 05_greedy-algorithms.mp4 |
18.66MB |
| 05_impact-of-classification.en.srt |
1.43KB |
| 05_impact-of-classification.en.txt |
933B |
| 05_impact-of-classification.mp4 |
4.04MB |
| 05_initializing-k-means.en.srt |
10.71KB |
| 05_initializing-k-means.en.txt |
6.96KB |
| 05_initializing-k-means.mp4 |
17.84MB |
| 05_lasso-demo.en.srt |
6.49KB |
| 05_lasso-demo.en.txt |
4.08KB |
| 05_lasso-demo.mp4 |
12.17MB |
| 05_lets-get-started.en.srt |
1.04KB |
| 05_lets-get-started.en.txt |
567B |
| 05_lets-get-started.mp4 |
2.76MB |
| 05_linear-classifier-model.en.srt |
6.85KB |
| 05_linear-classifier-model.en.txt |
4.20KB |
| 05_linear-classifier-model.mp4 |
19.07MB |
| 05_linear-classifiers.en.srt |
7.15KB |
| 05_linear-classifiers.en.txt |
4.37KB |
| 05_linear-classifiers.mp4 |
18.54MB |
| 05_machine-learning-is-changing-the-world.en.srt |
5.38KB |
| 05_machine-learning-is-changing-the-world.en.txt |
3.40KB |
| 05_machine-learning-is-changing-the-world.mp4 |
16.31MB |
| 05_module-4-recap.en.srt |
10.47KB |
| 05_module-4-recap.en.txt |
6.71KB |
| 05_module-4-recap.mp4 |
25.94MB |
| 05_module-by-module-topics-covered.en.srt |
13.51KB |
| 05_module-by-module-topics-covered.en.txt |
8.42KB |
| 05_module-by-module-topics-covered.mp4 |
28.97MB |
| 05_normalizing-weights.en.srt |
2.71KB |
| 05_normalizing-weights.en.txt |
1.69KB |
| 05_normalizing-weights.mp4 |
6.74MB |
| 05_optional-measuring-convergence.en.srt |
5.37KB |
| 05_optional-measuring-convergence.en.txt |
3.42KB |
| 05_optional-measuring-convergence.mp4 |
12.50MB |
| 05 optional-reading-worked-out-example-for-gradient-descent instructions html |
29.52KB |
| 05_outlining-the-first-half-of-the-course.en.srt |
7.23KB |
| 05_outlining-the-first-half-of-the-course.en.txt |
4.69KB |
| 05_outlining-the-first-half-of-the-course.mp4 |
14.84MB |
| 05_overfitting-for-more-general-multiple-regression-models.en.srt |
5.05KB |
| 05_overfitting-for-more-general-multiple-regression-models.en.txt |
3.16KB |
| 05_overfitting-for-more-general-multiple-regression-models.mp4 |
11.33MB |
| 05_overview-of-learning-logistic-regression-models.en.srt |
3.16KB |
| 05_overview-of-learning-logistic-regression-models.en.txt |
2.04KB |
| 05_overview-of-learning-logistic-regression-models.mp4 |
7.87MB |
| 05_prioritizing-important-words-with-tf-idf.en.srt |
4.95KB |
| 05_prioritizing-important-words-with-tf-idf.en.txt |
3.17KB |
| 05_prioritizing-important-words-with-tf-idf.mp4 |
13.51MB |
| 05_querying-for-the-most-similar-images-for-car-image.en.srt |
2.10KB |
| 05_querying-for-the-most-similar-images-for-car-image.en.txt |
1.22KB |
| 05_querying-for-the-most-similar-images-for-car-image.mp4 |
4.64MB |
| 05_regression-ml-block-diagram.en.srt |
5.81KB |
| 05_regression-ml-block-diagram.en.txt |
3.56KB |
| 05_regression-ml-block-diagram.mp4 |
11.74MB |
| 05_regularized-logistic-regression.en.srt |
8.65KB |
| 05_regularized-logistic-regression.en.txt |
4.66KB |
| 05_regularized-logistic-regression.mp4 |
20.90MB |
| 05_review-of-key-concepts.en.srt |
8.48KB |
| 05_review-of-key-concepts.en.txt |
4.51KB |
| 05_review-of-key-concepts.mp4 |
11.39MB |
| 05_ridge-regression-demo.en.srt |
12.01KB |
| 05_ridge-regression-demo.en.txt |
7.54KB |
| 05_ridge-regression-demo.mp4 |
24.95MB |
| 05_searching-neighboring-bins.en.srt |
10.59KB |
| 05_searching-neighboring-bins.en.txt |
6.80KB |
| 05_searching-neighboring-bins.mp4 |
22.02MB |
| 05_summary-of-gradient-ascent-for-logistic-regression.en.srt |
2.64KB |
| 05_summary-of-gradient-ascent-for-logistic-regression.en.txt |
1.58KB |
| 05_summary-of-gradient-ascent-for-logistic-regression.mp4 |
6.15MB |
| 05_tensorflow-implementation-of-content-based-filtering.en.srt |
7.29KB |
| 05_tensorflow-implementation-of-content-based-filtering.en.txt |
3.87KB |
| 05_tensorflow-implementation-of-content-based-filtering.mp4 |
12.94MB |
| 05_the-future-of-intelligent-applications.en.srt |
3.92KB |
| 05_the-future-of-intelligent-applications.en.txt |
2.20KB |
| 05_the-future-of-intelligent-applications.mp4 |
12.79MB |
| 05_to-normalize-or-not-and-other-distance-considerations.en.srt |
9.93KB |
| 05_to-normalize-or-not-and-other-distance-considerations.en.txt |
6.41KB |
| 05_to-normalize-or-not-and-other-distance-considerations.mp4 |
21.58MB |
| 05_training-test-split.en.srt |
2.54KB |
| 05_training-test-split.en.txt |
1.58KB |
| 05_training-test-split.mp4 |
6.10MB |
| 05_unsupervised-learning-part-2.en.srt |
5.81KB |
| 05_unsupervised-learning-part-2.en.txt |
3.09KB |
| 05_unsupervised-learning-part-2.mp4 |
8.25MB |
| 05_using-apply-for-data-transformation.en.srt |
6.37KB |
| 05_using-apply-for-data-transformation.en.txt |
3.75KB |
| 05_using-apply-for-data-transformation.mp4 |
12.46MB |
| 05_using-precision-recall-to-compare-recommender-models.en.srt |
4.35KB |
| 05_using-precision-recall-to-compare-recommender-models.en.txt |
2.60KB |
| 05_using-precision-recall-to-compare-recommender-models.mp4 |
11.25MB |
| 05_very-optional-deriving-gradient-of-log-likelihood.en.srt |
8.52KB |
| 05_very-optional-deriving-gradient-of-log-likelihood.en.txt |
5.10KB |
| 05_very-optional-deriving-gradient-of-log-likelihood.mp4 |
17.85MB |
| 05_visualizing-overfitting-with-high-degree-polynomial-features.en.srt |
5.01KB |
| 05_visualizing-overfitting-with-high-degree-polynomial-features.en.txt |
3.16KB |
| 05_visualizing-overfitting-with-high-degree-polynomial-features.mp4 |
9.93MB |
| 05_visualizing-scaling-behavior-of-kd-trees.en.srt |
5.38KB |
| 05_visualizing-scaling-behavior-of-kd-trees.en.txt |
3.43KB |
| 05_visualizing-scaling-behavior-of-kd-trees.mp4 |
10.15MB |
| 05_visualizing-the-cost-function.en.srt |
12.26KB |
| 05_visualizing-the-cost-function.en.txt |
6.35KB |
| 05_visualizing-the-cost-function.mp4 |
17.32MB |
| 05_when-to-use-decision-trees.en.srt |
10.46KB |
| 05_when-to-use-decision-trees.en.txt |
5.58KB |
| 05_when-to-use-decision-trees.mp4 |
17.47MB |
| 05_where-we-see-seasonality.en.srt |
4.76KB |
| 05_where-we-see-seasonality.en.txt |
3.08KB |
| 05_where-we-see-seasonality.mp4 |
11.26MB |
| 06_a-brief-recap.en.srt |
1.59KB |
| 06_a-brief-recap.en.txt |
1022B |
| 06_a-brief-recap.mp4 |
4.48MB |
| 06_algorithmic-summary-of-gradient-descent-approach.en.srt |
5.03KB |
| 06_algorithmic-summary-of-gradient-descent-approach.en.txt |
3.15KB |
| 06_algorithmic-summary-of-gradient-descent-approach.mp4 |
10.97MB |
| 06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.srt |
17.71KB |
| 06_algorithm-refinement-mini-batch-and-soft-updates-optional.en.txt |
9.44KB |
| 06_algorithm-refinement-mini-batch-and-soft-updates-optional.mp4 |
25.55MB |
| 06_approximate-k-nn-search-using-kd-trees.en.srt |
9.76KB |
| 06_approximate-k-nn-search-using-kd-trees.en.txt |
6.18KB |
| 06_approximate-k-nn-search-using-kd-trees.mp4 |
22.95MB |
| 06_assumed-background.en.srt |
10.29KB |
| 06_assumed-background.en.txt |
6.56KB |
| 06_assumed-background.mp4 |
19.43MB |
| 06_bias-variance-and-neural-networks.en.srt |
14.58KB |
| 06_bias-variance-and-neural-networks.en.txt |
9.46KB |
| 06_bias-variance-and-neural-networks.mp4 |
26.94MB |
| 06 building-exploring-a-nearest-neighbors-model-for-wikipedia-articles en srt |
3.66KB |
| 06 building-exploring-a-nearest-neighbors-model-for-wikipedia-articles en txt |
2.21KB |
| 06_building-exploring-a-nearest-neighbors-model-for-wikipedia-articles.mp4 |
9.38MB |
| 06_calculating-tf-idf-vectors.en.srt |
5.96KB |
| 06_calculating-tf-idf-vectors.en.txt |
3.68KB |
| 06_calculating-tf-idf-vectors.mp4 |
14.52MB |
| 06_choosing-stepsize-and-convergence-criteria.en.srt |
7.67KB |
| 06_choosing-stepsize-and-convergence-criteria.en.txt |
4.70KB |
| 06_choosing-stepsize-and-convergence-criteria.mp4 |
14.71MB |
| 06_choosing-the-number-of-clusters.en.srt |
11.16KB |
| 06_choosing-the-number-of-clusters.en.txt |
5.83KB |
| 06_choosing-the-number-of-clusters.mp4 |
16.85MB |
| 06_choosing-what-features-to-use.en.srt |
18.66KB |
| 06_choosing-what-features-to-use.en.txt |
11.93KB |
| 06_choosing-what-features-to-use.mp4 |
30.87MB |
| 06_comparing-the-approaches.en.srt |
2.13KB |
| 06_comparing-the-approaches.en.txt |
1.34KB |
| 06_comparing-the-approaches.mp4 |
6.22MB |
| 06_complexity-of-the-greedy-forward-stepwise-algorithm.en.srt |
3.71KB |
| 06_complexity-of-the-greedy-forward-stepwise-algorithm.en.txt |
2.29KB |
| 06_complexity-of-the-greedy-forward-stepwise-algorithm.mp4 |
8.40MB |
| 06_decision-boundaries.en.srt |
5.44KB |
| 06_decision-boundaries.en.txt |
3.36KB |
| 06_decision-boundaries.mp4 |
16.00MB |
| 06_deep-features.en.srt |
8.92KB |
| 06_deep-features.en.txt |
5.64KB |
| 06_deep-features.mp4 |
24.39MB |
| 06_displaying-other-example-image-retrievals-with-a-python-lambda.en.srt |
4.72KB |
| 06_displaying-other-example-image-retrievals-with-a-python-lambda.en.txt |
2.74KB |
| 06_displaying-other-example-image-retrievals-with-a-python-lambda.mp4 |
9.92MB |
| 06_effect-of-coefficient-values-on-decision-boundary.en.srt |
2.95KB |
| 06_effect-of-coefficient-values-on-decision-boundary.en.txt |
1.83KB |
| 06_effect-of-coefficient-values-on-decision-boundary.mp4 |
7.65MB |
| 06_fairness-bias-and-ethics.en.srt |
13.43KB |
| 06_fairness-bias-and-ethics.en.txt |
8.64KB |
| 06_fairness-bias-and-ethics.mp4 |
25.35MB |
| 06_hidden-markov-models.en.srt |
13.59KB |
| 06_hidden-markov-models.en.txt |
8.67KB |
| 06_hidden-markov-models.mp4 |
32.10MB |
| 06_jupyter-notebooks.en.srt |
7.33KB |
| 06_jupyter-notebooks.en.txt |
3.88KB |
| 06_jupyter-notebooks.mp4 |
19.90MB |
| 06_lsh-in-higher-dimensions.en.srt |
5.19KB |
| 06_lsh-in-higher-dimensions.en.txt |
3.27KB |
| 06_lsh-in-higher-dimensions.mp4 |
9.90MB |
| 06_optional-adding-regularization.en.srt |
4.02KB |
| 06_optional-adding-regularization.en.txt |
2.50KB |
| 06_optional-adding-regularization.mp4 |
10.85MB |
| 06_outlining-the-second-half-of-the-course.en.srt |
7.81KB |
| 06_outlining-the-second-half-of-the-course.en.txt |
5.10KB |
| 06_outlining-the-second-half-of-the-course.mp4 |
16.16MB |
| 06_polynomial-regression.en.srt |
9.44KB |
| 06_polynomial-regression.en.txt |
5.00KB |
| 06_polynomial-regression.mp4 |
22.84MB |
| 06_reading-software-tools-you-ll-need_instructions.html |
12.93KB |
| 06_reading-software-tools-you-ll-need_quickstart.html |
166.33KB |
| 06_regression-trees-optional.en.srt |
12.17KB |
| 06_regression-trees-optional.en.txt |
7.73KB |
| 06_regression-trees-optional.mp4 |
18.90MB |
| 06_regression-with-general-features-of-1-input.en.srt |
3.09KB |
| 06_regression-with-general-features-of-1-input.en.txt |
1.94KB |
| 06_regression-with-general-features-of-1-input.mp4 |
8.60MB |
| 06_running-gradient-descent.en.srt |
7.31KB |
| 06_running-gradient-descent.en.txt |
4.56KB |
| 06_running-gradient-descent.mp4 |
18.37MB |
| 06_the-ridge-coefficient-path.en.srt |
4.63KB |
| 06_the-ridge-coefficient-path.en.txt |
2.92KB |
| 06_the-ridge-coefficient-path.mp4 |
9.57MB |
| 06_training-a-sentiment-classifier.en.srt |
3.83KB |
| 06_training-a-sentiment-classifier.en.txt |
2.22KB |
| 06_training-a-sentiment-classifier.mp4 |
9.07MB |
| 06_visualization-examples.en.srt |
8.71KB |
| 06_visualization-examples.en.txt |
4.57KB |
| 06_visualization-examples.mp4 |
17.18MB |
| 06_visualizing-predictions-of-simple-model-with-matplotlib.en.srt |
5.35KB |
| 06_visualizing-predictions-of-simple-model-with-matplotlib.en.txt |
3.23KB |
| 06_visualizing-predictions-of-simple-model-with-matplotlib.mp4 |
11.87MB |
| 06_why-a-case-study-approach.en.srt |
10.92KB |
| 06_why-a-case-study-approach.en.txt |
6.86KB |
| 06_why-a-case-study-approach.mp4 |
29.06MB |
| 07_assumed-background.en.srt |
5.84KB |
| 07_assumed-background.en.txt |
3.76KB |
| 07_assumed-background.mp4 |
11.85MB |
| 07_evaluating-a-classifier-the-roc-curve.en.srt |
5.68KB |
| 07_evaluating-a-classifier-the-roc-curve.en.txt |
3.40KB |
| 07_evaluating-a-classifier-the-roc-curve.mp4 |
11.89MB |
| 07_examples-of-document-retrieval-in-action.en.srt |
4.80KB |
| 07_examples-of-document-retrieval-in-action.en.txt |
2.78KB |
| 07_examples-of-document-retrieval-in-action.mp4 |
12.94MB |
| 07_inspecting-the-model-coefficients-learned.en.srt |
1.52KB |
| 07_inspecting-the-model-coefficients-learned.en.txt |
923B |
| 07_inspecting-the-model-coefficients-learned.mp4 |
3.39MB |
| 07_optional-a-worked-out-example-for-kd-trees_instructions.html |
1.26MB |
| 07_optional-improving-efficiency-through-multiple-tables.en.srt |
25.96KB |
| 07_optional-improving-efficiency-through-multiple-tables.en.txt |
15.95KB |
| 07_optional-improving-efficiency-through-multiple-tables.mp4 |
54.20MB |
| 07_retrieving-similar-documents-using-nearest-neighbor-search.en.srt |
3.47KB |
| 07_retrieving-similar-documents-using-nearest-neighbor-search.en.txt |
2.22KB |
| 07_retrieving-similar-documents-using-nearest-neighbor-search.mp4 |
8.80MB |
| 07_software-tools-you-ll-need-for-this-course_instructions.html |
13.54KB |
| 07_software-tools-you-ll-need-for-this-course_quickstart.html |
166.33KB |
| 07_specialization-overview.en.srt |
9.24KB |
| 07_specialization-overview.en.txt |
5.93KB |
| 07_specialization-overview.mp4 |
27.28MB |
| 07_the-state-of-reinforcement-learning.en.srt |
4.04KB |
| 07_the-state-of-reinforcement-learning.en.txt |
2.63KB |
| 07_the-state-of-reinforcement-learning.mp4 |
7.86MB |
| 07_using-features-of-the-inputs.en.srt |
2.90KB |
| 07_using-features-of-the-inputs.en.txt |
1.85KB |
| 07_using-features-of-the-inputs.mp4 |
8.72MB |
| 08_a-big-week-ahead_instructions.html |
1.19KB |
| 08 applying-model-to-find-most-positive-negative-reviews-for-a-product en srt |
5.16KB |
| 08 applying-model-to-find-most-positive-negative-reviews-for-a-product en txt |
3.08KB |
| 08_applying-model-to-find-most-positive-negative-reviews-for-a-product.mp4 |
13.19MB |
| 08_exploring-other-features-of-the-data.en.srt |
7.38KB |
| 08_exploring-other-features-of-the-data.en.txt |
4.37KB |
| 08_exploring-other-features-of-the-data.mp4 |
13.66MB |
| 08_reading-software-tools-you-ll-need_instructions.html |
12.99KB |
| 08_reading-software-tools-you-ll-need_quickstart.html |
166.33KB |
| 09_exploring-the-most-positive-negative-aspects-of-a-product.en.srt |
5.84KB |
| 09_exploring-the-most-positive-negative-aspects-of-a-product.en.txt |
3.43KB |
| 09_exploring-the-most-positive-negative-aspects-of-a-product.mp4 |
15.78MB |
| 09_learning-a-model-to-predict-house-prices-from-more-features.en.srt |
3.61KB |
| 09_learning-a-model-to-predict-house-prices-from-more-features.en.txt |
2.12KB |
| 09_learning-a-model-to-predict-house-prices-from-more-features.mp4 |
7.73MB |
| 1 |
9B |
| 10 |
3B |
| 10_applying-learned-models-to-predict-price-of-an-average-house.en.srt |
5.45KB |
| 10_applying-learned-models-to-predict-price-of-an-average-house.en.txt |
3.24KB |
| 10_applying-learned-models-to-predict-price-of-an-average-house.mp4 |
12.65MB |
| 100 |
1.17MB |
| 101 |
1.33MB |
| 102 |
1.39MB |
| 103 |
1.60MB |
| 104 |
1.61MB |
| 105 |
1.66MB |
| 106 |
1.81MB |
| 107 |
1.92MB |
| 108 |
34.36KB |
| 109 |
100.11KB |
| 11 |
7B |
| 11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.srt |
8.24KB |
| 11_applying-learned-models-to-predict-price-of-two-fancy-houses.en.txt |
4.58KB |
| 11_applying-learned-models-to-predict-price-of-two-fancy-houses.mp4 |
22.37MB |
| 110 |
235.64KB |
| 111 |
522.47KB |
| 112 |
628.57KB |
| 113 |
738.28KB |
| 114 |
831.75KB |
| 115 |
1003.70KB |
| 116 |
1.05MB |
| 117 |
1.16MB |
| 118 |
1.33MB |
| 119 |
1.43MB |
| 12 |
10B |
| 120 |
1.52MB |
| 121 |
1.63MB |
| 122 |
1.65MB |
| 123 |
1.65MB |
| 124 |
1.72MB |
| 125 |
1.79MB |
| 126 |
1.79MB |
| 127 |
1.81MB |
| 128 |
1.81MB |
| 129 |
1.83MB |
| 13 |
67B |
| 130 |
1.85MB |
| 131 |
1.92MB |
| 132 |
1.94MB |
| 133 |
1.98MB |
| 134 |
133.37KB |
| 135 |
242.52KB |
| 136 |
245.90KB |
| 137 |
325.95KB |
| 138 |
378.81KB |
| 139 |
428.02KB |
| 14 |
48B |
| 140 |
450.06KB |
| 141 |
461.82KB |
| 142 |
486.26KB |
| 143 |
528.18KB |
| 144 |
537.42KB |
| 145 |
593.67KB |
| 146 |
614.16KB |
| 147 |
685.36KB |
| 148 |
756.93KB |
| 149 |
775.54KB |
| 15 |
2B |
| 150 |
838.24KB |
| 151 |
907.26KB |
| 152 |
944.60KB |
| 153 |
972.11KB |
| 154 |
978.74KB |
| 155 |
1.04MB |
| 156 |
1.09MB |
| 157 |
1.10MB |
| 158 |
1.11MB |
| 159 |
1.12MB |
| 16 |
2B |
| 160 |
1.19MB |
| 161 |
1.25MB |
| 162 |
1.25MB |
| 163 |
1.28MB |
| 164 |
1.31MB |
| 165 |
1.56MB |
| 166 |
1.57MB |
| 167 |
1.61MB |
| 168 |
1.63MB |
| 169 |
1.68MB |
| 17 |
3B |
| 170 |
1.69MB |
| 171 |
1.70MB |
| 172 |
1.73MB |
| 173 |
1.82MB |
| 174 |
1.84MB |
| 175 |
1.86MB |
| 176 |
1.88MB |
| 177 |
1.92MB |
| 178 |
30.17KB |
| 179 |
93.53KB |
| 18 |
10B |
| 180 |
106.22KB |
| 181 |
129.63KB |
| 182 |
161.40KB |
| 183 |
163.90KB |
| 184 |
167.27KB |
| 185 |
198.17KB |
| 186 |
230.55KB |
| 187 |
244.35KB |
| 188 |
290.50KB |
| 189 |
478.96KB |
| 19 |
9B |
| 190 |
494.77KB |
| 191 |
537.70KB |
| 192 |
548.84KB |
| 193 |
563.09KB |
| 194 |
585.08KB |
| 195 |
619.92KB |
| 196 |
629.70KB |
| 197 |
632.97KB |
| 198 |
633.96KB |
| 199 |
633.96KB |
| 2 |
12B |
| 20 |
38B |
| 200 |
633.96KB |
| 201 |
633.96KB |
| 202 |
633.96KB |
| 203 |
636.17KB |
| 204 |
636.17KB |
| 205 |
636.17KB |
| 206 |
636.17KB |
| 207 |
636.17KB |
| 208 |
657.63KB |
| 209 |
707.35KB |
| 21 |
48B |
| 210 |
717.23KB |
| 211 |
746.37KB |
| 212 |
904.63KB |
| 213 |
945.25KB |
| 214 |
947.25KB |
| 215 |
1007.50KB |
| 216 |
1.02MB |
| 217 |
1.04MB |
| 218 |
1.05MB |
| 219 |
1.06MB |
| 22 |
12B |
| 220 |
1.09MB |
| 221 |
1.10MB |
| 222 |
1.10MB |
| 223 |
1.11MB |
| 224 |
1.18MB |
| 225 |
1.25MB |
| 226 |
1.28MB |
| 227 |
1.34MB |
| 228 |
1.40MB |
| 229 |
1.44MB |
| 23 |
4B |
| 230 |
1.46MB |
| 231 |
1.46MB |
| 232 |
1.47MB |
| 233 |
1.47MB |
| 234 |
1.47MB |
| 235 |
1.47MB |
| 236 |
1.47MB |
| 237 |
1.49MB |
| 238 |
1.56MB |
| 239 |
1.59MB |
| 24 |
85B |
| 240 |
1.59MB |
| 241 |
1.60MB |
| 242 |
1.63MB |
| 243 |
1.64MB |
| 244 |
1.65MB |
| 245 |
1.70MB |
| 246 |
1.78MB |
| 247 |
1.89MB |
| 248 |
5.50KB |
| 249 |
15.90KB |
| 25 |
52B |
| 250 |
138.93KB |
| 251 |
153.94KB |
| 252 |
163.44KB |
| 253 |
176.64KB |
| 254 |
209.87KB |
| 255 |
247.93KB |
| 256 |
321.15KB |
| 257 |
325.26KB |
| 258 |
468.76KB |
| 259 |
490.96KB |
| 26 |
22B |
| 260 |
498.69KB |
| 261 |
540.84KB |
| 262 |
567.23KB |
| 263 |
630.03KB |
| 264 |
692.18KB |
| 265 |
736.36KB |
| 266 |
748.39KB |
| 267 |
760.58KB |
| 268 |
771.33KB |
| 269 |
800.48KB |
| 27 |
4B |
| 270 |
813.44KB |
| 271 |
839.63KB |
| 272 |
920.01KB |
| 273 |
967.67KB |
| 274 |
990.64KB |
| 275 |
996.34KB |
| 276 |
1012.11KB |
| 277 |
1012.95KB |
| 278 |
1.01MB |
| 279 |
1.06MB |
| 28 |
1B |
| 280 |
1.06MB |
| 281 |
1.08MB |
| 282 |
1.13MB |
| 283 |
1.15MB |
| 284 |
1.15MB |
| 285 |
1.18MB |
| 286 |
1.18MB |
| 287 |
1.27MB |
| 288 |
1.28MB |
| 289 |
1.31MB |
| 29 |
33B |
| 290 |
1.31MB |
| 291 |
1.35MB |
| 292 |
1.38MB |
| 293 |
1.38MB |
| 294 |
1.42MB |
| 295 |
1.45MB |
| 296 |
1.48MB |
| 297 |
1.52MB |
| 298 |
1.60MB |
| 299 |
1.62MB |
| 3 |
2B |
| 30 |
14B |
| 300 |
1.65MB |
| 301 |
1.69MB |
| 302 |
1.69MB |
| 303 |
1.72MB |
| 304 |
1.75MB |
| 305 |
1.78MB |
| 306 |
1.80MB |
| 307 |
1.84MB |
| 308 |
1.86MB |
| 309 |
1.94MB |
| 31 |
1B |
| 310 |
2.00MB |
| 311 |
34.29KB |
| 312 |
109.99KB |
| 313 |
116.23KB |
| 314 |
119.14KB |
| 315 |
189.88KB |
| 316 |
221.69KB |
| 317 |
271.54KB |
| 318 |
315.38KB |
| 319 |
350.77KB |
| 32 |
7B |
| 320 |
436.99KB |
| 321 |
445.28KB |
| 322 |
463.96KB |
| 323 |
470.93KB |
| 324 |
514.28KB |
| 325 |
541.93KB |
| 326 |
555.24KB |
| 327 |
569.42KB |
| 328 |
571.39KB |
| 329 |
632.29KB |
| 33 |
12B |
| 330 |
668.56KB |
| 331 |
760.29KB |
| 332 |
787.83KB |
| 333 |
847.58KB |
| 334 |
879.54KB |
| 335 |
909.30KB |
| 336 |
912.27KB |
| 337 |
969.94KB |
| 338 |
995.59KB |
| 339 |
1.02MB |
| 34 |
4B |
| 340 |
1.16MB |
| 341 |
1.18MB |
| 342 |
1.19MB |
| 343 |
1.21MB |
| 344 |
1.22MB |
| 345 |
1.22MB |
| 346 |
1.24MB |
| 347 |
1.29MB |
| 348 |
1.29MB |
| 349 |
1.32MB |
| 35 |
15B |
| 350 |
1.36MB |
| 351 |
1.41MB |
| 352 |
1.41MB |
| 353 |
1.44MB |
| 354 |
1.48MB |
| 355 |
1.50MB |
| 356 |
1.58MB |
| 357 |
1.59MB |
| 358 |
1.59MB |
| 359 |
1.60MB |
| 36 |
3B |
| 360 |
1.61MB |
| 361 |
1.61MB |
| 362 |
1.61MB |
| 363 |
1.62MB |
| 364 |
1.65MB |
| 365 |
1.67MB |
| 366 |
1.76MB |
| 367 |
1.82MB |
| 368 |
1.82MB |
| 369 |
1.83MB |
| 37 |
15B |
| 370 |
1.83MB |
| 371 |
1.87MB |
| 372 |
1.92MB |
| 373 |
1.93MB |
| 374 |
1.93MB |
| 375 |
1.99MB |
| 376 |
15.06KB |
| 377 |
60.52KB |
| 378 |
135.04KB |
| 379 |
139.18KB |
| 38 |
11B |
| 380 |
149.06KB |
| 381 |
192.68KB |
| 382 |
194.36KB |
| 383 |
218.21KB |
| 384 |
231.87KB |
| 385 |
253.59KB |
| 386 |
269.51KB |
| 387 |
342.23KB |
| 388 |
347.36KB |
| 389 |
354.27KB |
| 39 |
2B |
| 390 |
370.86KB |
| 391 |
395.90KB |
| 392 |
449.17KB |
| 393 |
452.41KB |
| 394 |
496.97KB |
| 395 |
503.44KB |
| 396 |
533.61KB |
| 397 |
559.74KB |
| 398 |
597.87KB |
| 399 |
634.58KB |
| 4 |
8B |
| 40 |
3B |
| 400 |
703.89KB |
| 401 |
721.48KB |
| 402 |
780.88KB |
| 403 |
782.81KB |
| 404 |
801.34KB |
| 405 |
814.19KB |
| 406 |
819.55KB |
| 407 |
820.29KB |
| 408 |
829.40KB |
| 409 |
866.66KB |
| 41 |
1B |
| 410 |
869.94KB |
| 411 |
944.18KB |
| 412 |
974.43KB |
| 413 |
1.05MB |
| 414 |
1.06MB |
| 415 |
1.06MB |
| 416 |
1.07MB |
| 417 |
1.07MB |
| 418 |
1.08MB |
| 419 |
1.10MB |
| 42 |
107B |
| 420 |
1.11MB |
| 421 |
1.15MB |
| 422 |
1.21MB |
| 423 |
1.24MB |
| 424 |
1.27MB |
| 425 |
1.27MB |
| 426 |
1.27MB |
| 427 |
1.27MB |
| 428 |
1.27MB |
| 429 |
1.27MB |
| 43 |
178B |
| 430 |
1.28MB |
| 431 |
1.28MB |
| 432 |
1.35MB |
| 433 |
1.35MB |
| 434 |
1.37MB |
| 435 |
1.39MB |
| 436 |
1.39MB |
| 437 |
1.40MB |
| 438 |
1.45MB |
| 439 |
1.49MB |
| 44 |
16B |
| 440 |
1.50MB |
| 441 |
1.51MB |
| 442 |
1.54MB |
| 443 |
1.61MB |
| 444 |
1.62MB |
| 445 |
1.64MB |
| 446 |
1.67MB |
| 447 |
1.69MB |
| 448 |
1.72MB |
| 449 |
1.76MB |
| 45 |
4B |
| 450 |
1.83MB |
| 451 |
1.89MB |
| 452 |
1.89MB |
| 453 |
1.90MB |
| 454 |
1.91MB |
| 455 |
1.93MB |
| 456 |
1.94MB |
| 457 |
1.98MB |
| 458 |
1.99MB |
| 459 |
8.76KB |
| 46 |
2B |
| 460 |
26.28KB |
| 461 |
44.35KB |
| 462 |
83.72KB |
| 463 |
113.71KB |
| 464 |
114.13KB |
| 465 |
133.46KB |
| 466 |
133.79KB |
| 467 |
137.54KB |
| 468 |
154.73KB |
| 469 |
229.10KB |
| 47 |
79B |
| 470 |
263.27KB |
| 471 |
268.15KB |
| 472 |
294.25KB |
| 473 |
334.25KB |
| 474 |
385.60KB |
| 475 |
429.70KB |
| 476 |
434.44KB |
| 477 |
447.12KB |
| 478 |
449.74KB |
| 479 |
476.18KB |
| 48 |
133B |
| 480 |
480.28KB |
| 481 |
517.54KB |
| 482 |
567.28KB |
| 483 |
583.03KB |
| 484 |
613.26KB |
| 485 |
618.72KB |
| 486 |
621.66KB |
| 487 |
688.00KB |
| 488 |
691.39KB |
| 489 |
708.59KB |
| 49 |
1B |
| 490 |
725.82KB |
| 491 |
749.22KB |
| 492 |
754.91KB |
| 493 |
758.94KB |
| 494 |
771.09KB |
| 495 |
829.84KB |
| 496 |
841.39KB |
| 497 |
844.74KB |
| 498 |
856.69KB |
| 499 |
865.89KB |
| 5 |
2B |
| 50 |
147B |
| 500 |
877.50KB |
| 501 |
919.18KB |
| 502 |
924.19KB |
| 503 |
949.66KB |
| 504 |
1.01MB |
| 505 |
1.03MB |
| 506 |
1.03MB |
| 507 |
1.10MB |
| 508 |
1.15MB |
| 509 |
1.15MB |
| 51 |
8B |
| 510 |
1.15MB |
| 511 |
1.15MB |
| 512 |
1.16MB |
| 513 |
1.21MB |
| 514 |
1.34MB |
| 515 |
1.34MB |
| 516 |
1.36MB |
| 517 |
1.40MB |
| 518 |
1.42MB |
| 519 |
1.44MB |
| 52 |
56B |
| 520 |
1.46MB |
| 521 |
1.46MB |
| 522 |
1.54MB |
| 523 |
1.59MB |
| 524 |
1.63MB |
| 525 |
1.63MB |
| 526 |
1.64MB |
| 527 |
1.79MB |
| 528 |
1.81MB |
| 529 |
1.81MB |
| 53 |
31B |
| 530 |
1.81MB |
| 531 |
1.85MB |
| 532 |
1.88MB |
| 533 |
1.94MB |
| 534 |
14.80KB |
| 535 |
57.17KB |
| 536 |
75.14KB |
| 537 |
86.87KB |
| 538 |
100.31KB |
| 539 |
102.89KB |
| 54 |
62.44KB |
| 540 |
104.28KB |
| 541 |
161.73KB |
| 542 |
203.77KB |
| 543 |
246.86KB |
| 544 |
365.18KB |
| 545 |
412.76KB |
| 546 |
445.09KB |
| 547 |
533.56KB |
| 548 |
540.92KB |
| 549 |
550.42KB |
| 55 |
1.90MB |
| 550 |
594.75KB |
| 551 |
611.04KB |
| 552 |
633.96KB |
| 553 |
636.42KB |
| 554 |
651.48KB |
| 555 |
661.47KB |
| 556 |
685.04KB |
| 557 |
685.99KB |
| 558 |
758.01KB |
| 559 |
859.91KB |
| 56 |
881.25KB |
| 560 |
859.91KB |
| 561 |
859.91KB |
| 562 |
948.98KB |
| 563 |
964.40KB |
| 564 |
1004.95KB |
| 565 |
1.09MB |
| 566 |
1.09MB |
| 567 |
1.16MB |
| 568 |
1.18MB |
| 569 |
1.18MB |
| 57 |
997.98KB |
| 570 |
1.18MB |
| 571 |
1.20MB |
| 572 |
1.22MB |
| 573 |
1.26MB |
| 574 |
1.28MB |
| 575 |
1.34MB |
| 576 |
1.35MB |
| 577 |
1.37MB |
| 578 |
1.40MB |
| 579 |
1.40MB |
| 58 |
1.03MB |
| 580 |
1.46MB |
| 581 |
1.53MB |
| 582 |
1.60MB |
| 583 |
1.61MB |
| 584 |
1.63MB |
| 585 |
1.73MB |
| 586 |
1.75MB |
| 587 |
1.77MB |
| 588 |
1.95MB |
| 589 |
2.00MB |
| 59 |
1.13MB |
| 590 |
3.75KB |
| 591 |
64.10KB |
| 592 |
133.16KB |
| 593 |
137.63KB |
| 594 |
144.60KB |
| 595 |
156.22KB |
| 596 |
214.69KB |
| 597 |
258.14KB |
| 598 |
273.76KB |
| 599 |
306.54KB |
| 6 |
12B |
| 60 |
1.99MB |
| 600 |
355.26KB |
| 601 |
361.31KB |
| 602 |
566.47KB |
| 603 |
595.31KB |
| 604 |
683.11KB |
| 605 |
689.71KB |
| 606 |
764.69KB |
| 607 |
837.62KB |
| 608 |
906.29KB |
| 609 |
928.42KB |
| 61 |
32.68KB |
| 610 |
963.49KB |
| 611 |
1022.87KB |
| 612 |
1.04MB |
| 613 |
1.05MB |
| 614 |
1.09MB |
| 615 |
1.11MB |
| 616 |
1.16MB |
| 617 |
1.18MB |
| 618 |
1.21MB |
| 619 |
1.26MB |
| 62 |
47.81KB |
| 620 |
1.29MB |
| 621 |
1.34MB |
| 622 |
1.42MB |
| 623 |
1.63MB |
| 624 |
1.65MB |
| 625 |
1.76MB |
| 626 |
1.76MB |
| 627 |
1.78MB |
| 628 |
1.81MB |
| 629 |
1.82MB |
| 63 |
95.63KB |
| 630 |
1.83MB |
| 631 |
1.85MB |
| 632 |
1.85MB |
| 633 |
1.90MB |
| 634 |
1.90MB |
| 635 |
1.98MB |
| 636 |
80.06KB |
| 637 |
87.98KB |
| 638 |
104.96KB |
| 639 |
107.98KB |
| 64 |
386.21KB |
| 640 |
127.58KB |
| 641 |
196.48KB |
| 642 |
221.01KB |
| 643 |
231.59KB |
| 644 |
303.58KB |
| 645 |
660.07KB |
| 646 |
684.03KB |
| 647 |
783.22KB |
| 648 |
820.71KB |
| 649 |
914.94KB |
| 65 |
448.32KB |
| 650 |
970.18KB |
| 651 |
982.61KB |
| 652 |
982.61KB |
| 653 |
982.61KB |
| 654 |
982.61KB |
| 655 |
1010.63KB |
| 656 |
1.04MB |
| 657 |
1.13MB |
| 658 |
1.15MB |
| 659 |
1.16MB |
| 66 |
505.08KB |
| 660 |
1.26MB |
| 661 |
1.30MB |
| 662 |
1.34MB |
| 663 |
1.34MB |
| 664 |
1.36MB |
| 665 |
1.38MB |
| 666 |
1.38MB |
| 667 |
1.47MB |
| 668 |
1.52MB |
| 669 |
1.55MB |
| 67 |
702.55KB |
| 670 |
1.58MB |
| 671 |
1.65MB |
| 672 |
1.96MB |
| 673 |
93.79KB |
| 674 |
170.14KB |
| 675 |
175.26KB |
| 676 |
192.71KB |
| 677 |
271.61KB |
| 678 |
310.21KB |
| 679 |
310.21KB |
| 68 |
752.38KB |
| 680 |
310.21KB |
| 681 |
310.21KB |
| 682 |
358.45KB |
| 683 |
583.43KB |
| 684 |
626.94KB |
| 685 |
700.06KB |
| 686 |
775.38KB |
| 687 |
921.55KB |
| 688 |
1014.16KB |
| 689 |
1017.84KB |
| 69 |
962.49KB |
| 690 |
1.15MB |
| 691 |
1.15MB |
| 692 |
1.18MB |
| 693 |
1.24MB |
| 694 |
1.44MB |
| 695 |
1.52MB |
| 696 |
1.79MB |
| 697 |
1.92MB |
| 7 |
1B |
| 70 |
992.68KB |
| 71 |
1015.95KB |
| 72 |
1.03MB |
| 73 |
1.33MB |
| 74 |
1.33MB |
| 75 |
1.57MB |
| 76 |
1.91MB |
| 77 |
1.98MB |
| 78 |
1.99MB |
| 79 |
734.35KB |
| 8 |
7B |
| 80 |
974.48KB |
| 81 |
1.06MB |
| 82 |
1.14MB |
| 83 |
1.30MB |
| 84 |
1.30MB |
| 85 |
1.34MB |
| 86 |
1.72MB |
| 87 |
1.89MB |
| 88 |
1.93MB |
| 89 |
16.34KB |
| 9 |
2B |
| 90 |
57.61KB |
| 91 |
457.68KB |
| 92 |
645.98KB |
| 93 |
667.53KB |
| 94 |
668.08KB |
| 95 |
668.08KB |
| 96 |
668.08KB |
| 97 |
749.48KB |
| 98 |
1.05MB |
| 99 |
1.17MB |
| TutsNode.net.txt |
63B |