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2.1 Scikit-Learn Documentation.html 108
15.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html 133
2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html 191
46.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html 191
7.1 Example Scikit-Learn Workflow Notebook.html 192
6.1 Scikit-Learn Reference Notebook.html 194
2.2 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html 197
46.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html 197
17. Quick Note Decision Trees.html 221
3. Quick Note Upcoming Video.html 390
5. Quick Note Upcoming Videos.html 1.0K
18. Quick Tip How ML Algorithms Work.srt 1.9K
47. Scikit-Learn Practice.html 2.1K
13. Note Correction in the upcoming video (splitting data).html 2.2K
42. Quick Tip Correlation Analysis.srt 3.1K
1. Section Overview.srt 4.1K
33. Evaluating A Regression Model 2 (MAE).srt 5.7K
26. Evaluating A Classification Model 1 (Accuracy).srt 5.9K
4. Refresher What Is Machine Learning.srt 6.3K
10. Quick Tip Clean, Transform, Reduce.srt 6.4K
35. Machine Learning Model Evaluation.html 7.1K
44. Saving And Loading A Model 2.srt 9.0K
23. Making Predictions With Our Model (Regression).srt 9.1K
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34. Evaluating A Regression Model 3 (MSE).srt 9.2K
20. Fitting A Model To The Data.srt 9.3K
43. Saving And Loading A Model.srt 9.8K
28. Evaluating A Classification Model 3 (ROC Curve).srt 10K
6. Scikit-learn Cheatsheet.srt 10K
2. Scikit-learn Introduction.srt 11K
30. Evaluating A Classification Model 5 (Confusion Matrix).srt 11K
22. predict() vs predict_proba().srt 12K
16. Choosing The Right Model For Your Data 2 (Regression).srt 12K
32. Evaluating A Regression Model 1 (R2 Score).srt 12K
9. Getting Your Data Ready Splitting Your Data.srt 12K
27. Evaluating A Classification Model 2 (ROC Curve).srt 12K
24. Evaluating A Machine Learning Model (Score).srt 13K
31. Evaluating A Classification Model 6 (Classification Report).srt 15K
38. Improving A Machine Learning Model.srt 15K
29. Evaluating A Classification Model 4 (Confusion Matrix).srt 15K
46. Putting It All Together 2.srt 16K
37. Evaluating A Model With Scikit-learn Functions.srt 16K
12. Getting Your Data Ready Handling Missing Values With Pandas.srt 17K
40. Tuning Hyperparameters 2.srt 17K
19. Choosing The Right Model For Your Data 3 (Classification).srt 17K
25. Evaluating A Machine Learning Model 2 (Cross Validation).srt 17K
36. Evaluating A Model With Cross Validation and Scoring Parameter.srt 18K
41. Tuning Hyperparameters 3.srt 19K
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9.1 scikit-learn-data.zip 21K
15. Choosing The Right Model For Your Data.srt 21K
11. Getting Your Data Ready Convert Data To Numbers.srt 23K
14. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt 23K
8. Optional Debugging Warnings In Jupyter.srt 26K
45. Putting It All Together.srt 26K
39. Tuning Hyperparameters.srt 31K
7. Typical scikit-learn Workflow.srt 32K
![[VID]](/icons/movie.gif)
18. Quick Tip How ML Algorithms Work.mp4 11M
1. Section Overview.mp4 12M
10. Quick Tip Clean, Transform, Reduce.mp4 17M
42. Quick Tip Correlation Analysis.mp4 17M
33. Evaluating A Regression Model 2 (MAE).mp4 29M
26. Evaluating A Classification Model 1 (Accuracy).mp4 31M
2. Scikit-learn Introduction.mp4 41M
23. Making Predictions With Our Model (Regression).mp4 45M
28. Evaluating A Classification Model 3 (ROC Curve).mp4 51M
43. Saving And Loading A Model.mp4 53M
22. predict() vs predict_proba().mp4 54M
34. Evaluating A Regression Model 3 (MSE).mp4 55M
20. Fitting A Model To The Data.mp4 57M
44. Saving And Loading A Model 2.mp4 57M
30. Evaluating A Classification Model 5 (Confusion Matrix).mp4 64M
9. Getting Your Data Ready Splitting Your Data.mp4 64M
27. Evaluating A Classification Model 2 (ROC Curve).mp4 66M
21. Making Predictions With Our Model.mp4 67M
21. Making Predictions With Our Model.srt 67M
32. Evaluating A Regression Model 1 (R2 Score).mp4 70M
6. Scikit-learn Cheatsheet.mp4 75M
29. Evaluating A Classification Model 4 (Confusion Matrix).mp4 78M
16. Choosing The Right Model For Your Data 2 (Regression).mp4 87M
24. Evaluating A Machine Learning Model (Score).mp4 87M
31. Evaluating A Classification Model 6 (Classification Report).mp4 87M
4. Refresher What Is Machine Learning.mp4 88M
38. Improving A Machine Learning Model.mp4 91M
36. Evaluating A Model With Cross Validation and Scoring Parameter.mp4 92M
37. Evaluating A Model With Scikit-learn Functions.mp4 95M
25. Evaluating A Machine Learning Model 2 (Cross Validation).mp4 96M
12. Getting Your Data Ready Handling Missing Values With Pandas.mp4 105M
40. Tuning Hyperparameters 2.mp4 117M
46. Putting It All Together 2.mp4 117M
19. Choosing The Right Model For Your Data 3 (Classification).mp4 119M
41. Tuning Hyperparameters 3.mp4 122M
11. Getting Your Data Ready Convert Data To Numbers.mp4 135M
14. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4 137M
15. Choosing The Right Model For Your Data.mp4 143M
45. Putting It All Together.mp4 158M
39. Tuning Hyperparameters.mp4 176M
8. Optional Debugging Warnings In Jupyter.mp4 176M
7. Typical scikit-learn Workflow.mp4 190M