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585б |
| 0 |
890.79Кб |
| 1 |
939.58Кб |
| 10 |
718.23Кб |
| 100 |
687.47Кб |
| 100 - Chapter 20. Network-driven supervised machine learning.mp4 |
48.95Мб |
| 101 |
827.46Кб |
| 101 - Chapter 20. The basics of supervised machine learning.mp4 |
49.20Мб |
| 102 |
7.51Кб |
| 102 - Chapter 20. Measuring predicted label accuracy, Part 1.mp4 |
37.28Мб |
| 103 |
10.78Кб |
| 103 - Chapter 20. Measuring predicted label accuracy, Part 2.mp4 |
55.24Мб |
| 104 |
774.91Кб |
| 104 - Chapter 20. Optimizing KNN performance.mp4 |
35.68Мб |
| 105 |
454.37Кб |
| 105 - Chapter 20. Running a grid search using scikit-learn.mp4 |
39.33Мб |
| 106 |
589.82Кб |
| 106 - Chapter 20. Limitations of the KNN algorithm.mp4 |
63.16Мб |
| 107 |
740.84Кб |
| 107 - Chapter 21. Training linear classifiers with logistic regression.mp4 |
58.26Мб |
| 108 |
427.51Кб |
| 108 - Chapter 21. Training a linear classifier, Part 1.mp4 |
43.52Мб |
| 109 |
663.19Кб |
| 109 - Chapter 21. Training a linear classifier, Part 2.mp4 |
73.26Мб |
| 10 - Chapter 3. Using permutations to shuffle cards.mp4 |
35.40Мб |
| 11 |
755.90Кб |
| 110 |
735.31Кб |
| 110 - Chapter 21. Improving linear classification with logistic regression, Part 1.mp4 |
43.42Мб |
| 111 |
328.39Кб |
| 111 - Chapter 21. Improving linear classification with logistic regression, Part 2.mp4 |
43.12Мб |
| 112 |
617.38Кб |
| 112 - Chapter 21. Training linear classifiers using scikit-learn.mp4 |
49.64Мб |
| 113 |
376.21Кб |
| 113 - Chapter 21. Measuring feature importance with coefficients.mp4 |
93.13Мб |
| 114 |
632.63Кб |
| 114 - Chapter 22. Training nonlinear classifiers with decision tree techniques.mp4 |
65.20Мб |
| 115 |
744.55Кб |
| 115 - Chapter 22. Training a nested if_else model using two features.mp4 |
53.25Мб |
| 116 |
413.05Кб |
| 116 - Chapter 22. Deciding which feature to split on.mp4 |
57.23Мб |
| 117 |
784.19Кб |
| 117 - Chapter 22. Training if_else models with more than two features.mp4 |
57.79Мб |
| 118 |
57.11Кб |
| 118 - Chapter 22. Training decision tree classifiers using scikit-learn.mp4 |
51.86Мб |
| 119 |
905.53Кб |
| 119 - Chapter 22. Studying cancerous cells using feature importance.mp4 |
59.29Мб |
| 11 - Chapter 4. Case study 1 solution.mp4 |
34.27Мб |
| 12 |
840.00Кб |
| 120 |
554.27Кб |
| 120 - Chapter 22. Improving performance using random forest classification.mp4 |
57.38Мб |
| 121 |
619.10Кб |
| 121 - Chapter 22. Training random forest classifiers using scikit-learn.mp4 |
52.96Мб |
| 122 |
78.31Кб |
| 122 - Chapter 23. Case study 5 solution.mp4 |
32.94Мб |
| 123 |
235.59Кб |
| 123 - Chapter 23. Exploring the experimental observations.mp4 |
38.99Мб |
| 124 |
874.03Кб |
| 124 - Chapter 23. Training a predictive model using network features, Part 1.mp4 |
52.59Мб |
| 125 |
53.07Кб |
| 125 - Chapter 23. Training a predictive model using network features, Part 2.mp4 |
53.87Мб |
| 126 |
110.25Кб |
| 126 - Chapter 23. Adding profile features to the model.mp4 |
62.03Мб |
| 127 - Chapter 23. Optimizing performance across a steady set of features.mp4 |
42.55Мб |
| 128 - Chapter 23. Interpreting the trained model.mp4 |
64.17Мб |
| 12 - Chapter 4. Optimizing strategies using the sample space for a 10-card deck.mp4 |
47.10Мб |
| 13 |
284.28Кб |
| 13 - Case study 2 - Assessing online ad clicks for significance.mp4 |
31.40Мб |
| 14 |
642.27Кб |
| 14 - Chapter 5. Basic probability and statistical analysis using SciPy.mp4 |
76.23Мб |
| 15 |
833.63Кб |
| 15 - Chapter 5. Mean as a measure of centrality.mp4 |
36.58Мб |
| 16 |
218.31Кб |
| 16 - Chapter 5. Variance as a measure of dispersion.mp4 |
73.89Мб |
| 17 |
406.40Кб |
| 17 - Chapter 6. Making predictions using the central limit theorem and SciPy.mp4 |
58.61Мб |
| 18 |
714.45Кб |
| 18 - Chapter 6. Comparing two sampled normal curves.mp4 |
31.46Мб |
| 19 |
466.94Кб |
| 19 - Chapter 6. Determining the mean and variance of a population through random sampling.mp4 |
55.19Мб |
| 1 - Case study 1 - Finding the winning strategy in a card game.mp4 |
6.89Мб |
| 2 |
965.65Кб |
| 20 |
436.39Кб |
| 20 - Chapter 6. Computing the area beneath a normal curve.mp4 |
64.57Мб |
| 21 |
820.39Кб |
| 21 - Chapter 7. Statistical hypothesis testing.mp4 |
39.19Мб |
| 22 |
291.63Кб |
| 22 - Chapter 7. Assessing the divergence between sample mean and population mean.mp4 |
68.30Мб |
| 23 |
439.97Кб |
| 23 - Chapter 7. Data dredging - Coming to false conclusions through oversampling.mp4 |
79.88Мб |
| 24 |
850.33Кб |
| 24 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 1.mp4 |
53.28Мб |
| 25 |
859.42Кб |
| 25 - Chapter 7. Bootstrapping with replacement - Testing a hypothesis when the population variance is unknown 2.mp4 |
52.78Мб |
| 26 |
56.04Кб |
| 26 - Chapter 7. Permutation testing - Comparing means of samples when the population parameters are unknown.mp4 |
43.69Мб |
| 27 |
670.01Кб |
| 27 - Chapter 8. Analyzing tables using Pandas.mp4 |
40.87Мб |
| 28 |
997.70Кб |
| 28 - Chapter 8. Retrieving table rows.mp4 |
38.24Мб |
| 29 |
268.60Кб |
| 29 - Chapter 8. Saving and loading table data.mp4 |
40.28Мб |
| 2 - Chapter 1. Computing probabilities using Python This section covers.mp4 |
56.75Мб |
| 3 |
618.37Кб |
| 30 |
619.14Кб |
| 30 - Chapter 9. Case study 2 solution.mp4 |
33.60Мб |
| 31 |
822.06Кб |
| 31 - Chapter 9. Determining statistical significance.mp4 |
43.58Мб |
| 32 |
115.18Кб |
| 32 - Case study 3 - Tracking disease outbreaks using news headlines.mp4 |
6.60Мб |
| 33 |
779.81Кб |
| 33 - Chapter 10. Clustering data into groups.mp4 |
61.40Мб |
| 34 |
968.05Кб |
| 34 - Chapter 10. K-means - A clustering algorithm for grouping data into K central groups.mp4 |
61.20Мб |
| 35 |
357.36Кб |
| 35 - Chapter 10. Using density to discover clusters.mp4 |
52.23Мб |
| 36 |
724.65Кб |
| 36 - Chapter 10. Clustering based on non-Euclidean distance.mp4 |
68.79Мб |
| 37 |
101.59Кб |
| 37 - Chapter 10. Analyzing clusters using Pandas.mp4 |
40.48Мб |
| 38 |
173.25Кб |
| 38 - Chapter 11. Geographic location visualization and analysis.mp4 |
46.58Мб |
| 39 |
403.17Кб |
| 39 - Chapter 11. Plotting maps using Cartopy.mp4 |
33.23Мб |
| 3 - Chapter 1. Problem 2 - Analyzing multiple die rolls.mp4 |
60.89Мб |
| 4 |
118.37Кб |
| 40 |
743.59Кб |
| 40 - Chapter 11. Visualizing maps.mp4 |
58.27Мб |
| 41 |
753.44Кб |
| 41 - Chapter 11. Location tracking using GeoNamesCache.mp4 |
62.35Мб |
| 42 |
15.05Кб |
| 42 - Chapter 11. Limitations of the GeoNamesCache library.mp4 |
69.19Мб |
| 43 |
213.06Кб |
| 43 - Chapter 12. Case study 3 solution.mp4 |
34.63Мб |
| 44 |
379.70Кб |
| 44 - Chapter 12. Visualizing and clustering the extracted location data.mp4 |
70.72Мб |
| 45 |
628.04Кб |
| 45 - Case study 4 - Using online job postings to improve your data science resume.mp4 |
23.95Мб |
| 46 |
637.24Кб |
| 46 - Chapter 13. Measuring text similarities.mp4 |
36.28Мб |
| 47 |
783.40Кб |
| 47 - Chapter 13. Simple text comparison.mp4 |
44.00Мб |
| 48 |
252.02Кб |
| 48 - Chapter 13. Replacing words with numeric values.mp4 |
42.07Мб |
| 49 |
414.79Кб |
| 49 - Chapter 13. Vectorizing texts using word counts.mp4 |
44.50Мб |
| 4 - Chapter 2. Plotting probabilities using Matplotlib.mp4 |
53.74Мб |
| 5 |
791.22Кб |
| 50 |
775.76Кб |
| 50 - Chapter 13. Using normalization to improve TF vector similarity.mp4 |
48.56Мб |
| 51 |
824.74Кб |
| 51 - Chapter 13. Using unit vector dot products to convert between relevance metrics.mp4 |
41.64Мб |
| 52 |
571.96Кб |
| 52 - Chapter 13. Basic matrix operations, Part 1.mp4 |
48.78Мб |
| 53 |
138.01Кб |
| 53 - Chapter 13. Basic matrix operations, Part 2.mp4 |
27.15Мб |
| 54 |
264.47Кб |
| 54 - Chapter 13. Computational limits of matrix multiplication.mp4 |
47.81Мб |
| 55 |
737.98Кб |
| 55 - Chapter 14. Dimension reduction of matrix data.mp4 |
61.74Мб |
| 56 |
763.31Кб |
| 56 - Chapter 14. Reducing dimensions using rotation, Part 1.mp4 |
38.99Мб |
| 57 |
958.02Кб |
| 57 - Chapter 14. Reducing dimensions using rotation, Part 2.mp4 |
37.56Мб |
| 58 |
14.01Кб |
| 58 - Chapter 14. Dimension reduction using PCA and scikit-learn.mp4 |
64.72Мб |
| 59 |
39.88Кб |
| 59 - Chapter 14. Clustering 4D data in two dimensions.mp4 |
54.44Мб |
| 5 - Chapter 2. Comparing multiple coin-flip probability distributions.mp4 |
65.57Мб |
| 6 |
812.11Кб |
| 60 |
228.00Кб |
| 60 - Chapter 14. Limitations of PCA.mp4 |
30.77Мб |
| 61 |
415.60Кб |
| 61 - Chapter 14. Computing principal components without rotation.mp4 |
47.80Мб |
| 62 |
786.46Кб |
| 62 - Chapter 14. Extracting eigenvectors using power iteration, Part 1.mp4 |
44.67Мб |
| 63 |
144.96Кб |
| 63 - Chapter 14. Extracting eigenvectors using power iteration, Part 2.mp4 |
34.38Мб |
| 64 |
444.56Кб |
| 64 - Chapter 14. Efficient dimension reduction using SVD and scikit-learn.mp4 |
68.60Мб |
| 65 |
369.03Кб |
| 65 - Chapter 15. NLP analysis of large text datasets.mp4 |
47.16Мб |
| 66 |
817.63Кб |
| 66 - Chapter 15. Vectorizing documents using scikit-learn.mp4 |
87.06Мб |
| 67 |
982.68Кб |
| 67 - Chapter 15. Ranking words by both post frequency and count, Part 1.mp4 |
56.59Мб |
| 68 |
46.62Кб |
| 68 - Chapter 15. Ranking words by both post frequency and count, Part 2.mp4 |
48.13Мб |
| 69 |
230.07Кб |
| 69 - Chapter 15. Computing similarities across large document datasets.mp4 |
60.24Мб |
| 6 - Chapter 3. Running random simulations in NumPy.mp4 |
36.35Мб |
| 7 |
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| 70 |
451.69Кб |
| 70 - Chapter 15. Clustering texts by topic, Part 1.mp4 |
73.30Мб |
| 71 |
653.80Кб |
| 71 - Chapter 15. Clustering texts by topic, Part 2.mp4 |
87.08Мб |
| 72 |
890.39Кб |
| 72 - Chapter 15. Visualizing text clusters.mp4 |
58.90Мб |
| 73 |
199.54Кб |
| 73 - Chapter 15. Using subplots to display multiple word clouds, Part 1.mp4 |
50.57Мб |
| 74 |
207.09Кб |
| 74 - Chapter 15. Using subplots to display multiple word clouds, Part 2.mp4 |
58.83Мб |
| 75 |
415.57Кб |
| 75 - Chapter 16. Extracting text from web pages.mp4 |
39.55Мб |
| 76 |
856.97Кб |
| 76 - Chapter 16. The structure of HTML documents.mp4 |
62.95Мб |
| 77 |
923.38Кб |
| 77 - Chapter 16. Parsing HTML using Beautiful Soup, Part 1.mp4 |
40.42Мб |
| 78 |
230.11Кб |
| 78 - Chapter 16. Parsing HTML using Beautiful Soup, Part 2.mp4 |
46.78Мб |
| 79 |
428.93Кб |
| 79 - Chapter 17. Case study 4 solution.mp4 |
37.42Мб |
| 7 - Chapter 3. Computing confidence intervals using histograms and NumPy arrays.mp4 |
47.59Мб |
| 8 |
119.94Кб |
| 80 |
336.42Кб |
| 80 - Chapter 17. Exploring the HTML for skill descriptions.mp4 |
59.65Мб |
| 81 |
347.83Кб |
| 81 - Chapter 17. Filtering jobs by relevance.mp4 |
73.18Мб |
| 82 |
512.96Кб |
| 82 - Chapter 17. Clustering skills in relevant job postings.mp4 |
66.54Мб |
| 83 |
804б |
| 83 - Chapter 17. Investigating the technical skill clusters.mp4 |
41.46Мб |
| 84 |
312.69Кб |
| 84 - Chapter 17. Exploring clusters at alternative values of K.mp4 |
69.37Мб |
| 85 |
432.10Кб |
| 85 - Chapter 17. Analyzing the 700 most relevant postings.mp4 |
40.95Мб |
| 86 |
489.73Кб |
| 86 - Case study 5 - Predicting future friendships from social network data.mp4 |
80.40Мб |
| 87 |
595.42Кб |
| 87 - Chapter 18. An introduction to graph theory and network analysis.mp4 |
74.88Мб |
| 88 |
900.74Кб |
| 88 - Chapter 18. Analyzing web networks using NetworkX, Part 1.mp4 |
30.92Мб |
| 89 |
457.18Кб |
| 89 - Chapter 18. Analyzing web networks using NetworkX, Part 2.mp4 |
53.06Мб |
| 8 - Chapter 3. Deriving probabilities from histograms.mp4 |
57.63Мб |
| 9 |
113.81Кб |
| 90 |
949.66Кб |
| 90 - Chapter 18. Utilizing undirected graphs to optimize the travel time between towns.mp4 |
57.39Мб |
| 91 |
366.51Кб |
| 91 - Chapter 18. Computing the fastest travel time between nodes, Part 1.mp4 |
32.12Мб |
| 92 |
552.72Кб |
| 92 - Chapter 18. Computing the fastest travel time between nodes, Part 2.mp4 |
49.04Мб |
| 93 |
53.45Кб |
| 93 - Chapter 19. Dynamic graph theory techniques for node ranking and social network analysis.mp4 |
75.08Мб |
| 94 |
133.05Кб |
| 94 - Chapter 19. Computing travel probabilities using matrix multiplication.mp4 |
40.21Мб |
| 95 |
527.65Кб |
| 95 - Chapter 19. Deriving PageRank centrality from probability theory.mp4 |
48.36Мб |
| 96 |
592.36Кб |
| 96 - Chapter 19. Computing PageRank centrality using NetworkX.mp4 |
44.66Мб |
| 97 |
733.27Кб |
| 97 - Chapter 19. Community detection using Markov clustering, Part 1.mp4 |
60.05Мб |
| 98 |
811.60Кб |
| 98 - Chapter 19. Community detection using Markov clustering, Part 2.mp4 |
75.21Мб |
| 99 |
457.38Кб |
| 99 - Chapter 19. Uncovering friend groups in social networks.mp4 |
57.99Мб |
| 9 - Chapter 3. Computing histograms in NumPy.mp4 |
52.99Мб |
| TutsNode.com.txt |
63б |