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.DS_Store |
6.00Кб |
.DS_Store |
6.00Кб |
.DS_Store |
6.00Кб |
.DS_Store |
6.00Кб |
.DS_Store |
6.00Кб |
.DS_Store |
6.00Кб |
0_getting.py |
303б |
198810101_20151231.csv |
543.79Кб |
1decision_tree_submit.py |
9.81Кб |
1email_spam_tfidf_submit.py |
2.62Кб |
1histogram.py |
529б |
1imputation.py |
3.25Кб |
1one_hot_encode.py |
2.42Кб |
1stock_price_prediction.py |
7.48Кб |
20051201_20151210.csv |
644б |
2avazu_ctr.py |
2.06Кб |
2clean_words.py |
723б |
2feature_selection.py |
1.12Кб |
2linear_regression.py |
4.72Кб |
2logistic_function.py |
833б |
2topic_categorization.py |
5.04Кб |
3decision_tree_regression.py |
7.08Кб |
3dimensionality_reduction.py |
635б |
3logistic_regression_from_scratch.py |
7.60Кб |
3plot_rbf_kernels.py |
1.13Кб |
3post_clustering.py |
919б |
4ctg.py |
1.14Кб |
4generic_feature_engineering.py |
344б |
4random_forest_feature_selection.py |
1.68Кб |
4support_vector_regression.py |
439б |
4topic_model.py |
998б |
5save_reuse_monitor_model.py |
1.00Кб |
5scikit_logistic_regression.py |
5.24Кб |
Best Practices in Data Preparation Stage.mp4 |
31.86Мб |
Best Practices in the Deployment and Monitoring Stage.mp4 |
13.90Мб |
Best Practices in the Model Training, Evaluation, and Selection Stage.mp4 |
10.84Мб |
Best Practices in the Training Sets Generation Stage.mp4 |
20.46Мб |
Brief Overview of Advertising Click-Through Prediction.mp4 |
11.00Мб |
Brief Overview of the Stock Market And Stock Price.mp4 |
7.05Мб |
Choosing Between the Linear and the RBF Kernel.mp4 |
14.21Мб |
Classifier Performance Evaluation.mp4 |
36.98Мб |
Click-Through Prediction with Decision Tree.mp4 |
24.99Мб |
Click-Through Prediction with Logistic Regression by Gradient Descent.mp4 |
75.32Мб |
Clustering.mp4 |
10.44Мб |
config.py |
3.38Кб |
CTG.xls |
1.66Мб |
Data Acquisition and Feature Generation.mp4 |
12.29Мб |
Data Preprocessing.mp4 |
9.15Мб |
Decision Tree Classifier.mp4 |
36.69Мб |
Decision Tree Regression.mp4 |
27.45Мб |
email_spam.py |
10.28Кб |
Exploring Naïve Bayes.mp4 |
5.10Мб |
Feature Selection via Random Forest.mp4 |
16.04Мб |
Fetal State Classification with SVM.mp4 |
21.84Мб |
Getting Started with Classification.mp4 |
8.76Мб |
Getting the Newsgroups Data.mp4 |
14.10Мб |
globalnames |
1011б |
history |
14б |
Installing Software and Setting Up.mp4 |
22.01Мб |
Introduction to Machine Learning.mp4 |
13.02Мб |
Linear Regression.mp4 |
30.28Мб |
Logistic Regression Classifier.mp4 |
37.40Мб |
Machine Learning with Python.mp4 |
1.61Мб |
Machine Learning with Python.mp4 |
1.61Мб |
Model Tuning and cross-validation.mp4 |
18.26Мб |
News topic Classification with Support Vector Machine.mp4 |
36.37Мб |
objectdb |
2.21Кб |
One-Hot Encoding - Converting Categorical Features to Numerical.mp4 |
21.42Мб |
Predicting Stock Price with Regression Algorithms.mp4 |
24.42Мб |
Random Forest - Feature Bagging of Decision Tree.mp4 |
18.28Мб |
Recap and Inverse Document Frequency.mp4 |
16.63Мб |
Regression Performance Evaluation.mp4 |
12.72Мб |
Stock Price Prediction with Regression Algorithms.mp4 |
34.23Мб |
Support Vector Regression.mp4 |
8.09Мб |
The Course Overview.mp4 |
17.18Мб |
The Implementations of Decision Tree.mp4 |
22.81Мб |
The Implementations of SVM.mp4 |
19.68Мб |
The Kernels of SVM.mp4 |
11.79Мб |
The Mechanics of Naïve Bayes.mp4 |
7.33Мб |
The Mechanics of SVM.mp4 |
9.16Мб |
The Naïve Bayes Implementation.mp4 |
57.36Мб |
Thinking about Features.mp4 |
20.34Мб |
Topic Modeling.mp4 |
13.04Мб |
Touring Powerful NLP Libraries in Python.mp4 |
40.29Мб |
Understanding NLP.mp4 |
15.97Мб |
Visualization.mp4 |
11.53Мб |