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7. Typical scikit-learn Workflow.mp4190.2 MiB2024-Jul-08 18:00
8. Optional Debugging Warnings In Jupyter.mp4176.1 MiB2024-Jul-08 17:56
39. Tuning Hyperparameters.mp4175.6 MiB2024-Jul-08 18:05
45. Putting It All Together.mp4158.4 MiB2024-Jul-08 17:47
15. Choosing The Right Model For Your Data.mp4143.3 MiB2024-Jul-08 17:54
14. Getting Your Data Ready Handling Missing Values With Scikit-learn.mp4136.9 MiB2024-Jul-08 18:01
11. Getting Your Data Ready Convert Data To Numbers.mp4135.0 MiB2024-Jul-08 18:04
41. Tuning Hyperparameters 3.mp4121.8 MiB2024-Jul-08 17:57
19. Choosing The Right Model For Your Data 3 (Classification).mp4118.9 MiB2024-Jul-08 17:53
46. Putting It All Together 2.mp4116.9 MiB2024-Jul-08 18:05
40. Tuning Hyperparameters 2.mp4116.8 MiB2024-Jul-08 17:42
12. Getting Your Data Ready Handling Missing Values With Pandas.mp4104.9 MiB2024-Jul-08 18:04
25. Evaluating A Machine Learning Model 2 (Cross Validation).mp496.0 MiB2024-Jul-08 17:55
37. Evaluating A Model With Scikit-learn Functions.mp494.8 MiB2024-Jul-08 17:47
36. Evaluating A Model With Cross Validation and Scoring Parameter.mp491.5 MiB2024-Jul-08 17:31
38. Improving A Machine Learning Model.mp490.9 MiB2024-Jul-08 17:58
4. Refresher What Is Machine Learning.mp488.3 MiB2024-Jul-08 18:00
31. Evaluating A Classification Model 6 (Classification Report).mp487.3 MiB2024-Jul-08 17:43
24. Evaluating A Machine Learning Model (Score).mp487.1 MiB2024-Jul-08 17:45
16. Choosing The Right Model For Your Data 2 (Regression).mp486.9 MiB2024-Jul-08 17:51
29. Evaluating A Classification Model 4 (Confusion Matrix).mp477.7 MiB2024-Jul-08 17:56
6. Scikit-learn Cheatsheet.mp475.1 MiB2024-Jul-08 13:44
32. Evaluating A Regression Model 1 (R2 Score).mp470.4 MiB2024-Jul-08 17:30
21. Making Predictions With Our Model.srt66.5 MiB2024-Jul-08 18:02
21. Making Predictions With Our Model.mp466.5 MiB2024-Jul-08 17:38
27. Evaluating A Classification Model 2 (ROC Curve).mp466.0 MiB2024-Jul-08 17:38
9. Getting Your Data Ready Splitting Your Data.mp463.7 MiB2024-Jul-08 17:56
30. Evaluating A Classification Model 5 (Confusion Matrix).mp463.6 MiB2024-Jul-08 17:56
44. Saving And Loading A Model 2.mp456.8 MiB2024-Jul-08 17:14
20. Fitting A Model To The Data.mp456.6 MiB2024-Jul-08 17:40
34. Evaluating A Regression Model 3 (MSE).mp454.9 MiB2024-Jul-08 17:49
22. predict() vs predict_proba().mp454.3 MiB2024-Jul-08 17:20
43. Saving And Loading A Model.mp452.6 MiB2024-Jul-08 17:29
28. Evaluating A Classification Model 3 (ROC Curve).mp450.6 MiB2024-Jul-08 17:31
23. Making Predictions With Our Model (Regression).mp444.9 MiB2024-Jul-08 17:55
2. Scikit-learn Introduction.mp440.6 MiB2024-Jul-08 17:25
26. Evaluating A Classification Model 1 (Accuracy).mp431.4 MiB2024-Jul-08 17:55
33. Evaluating A Regression Model 2 (MAE).mp428.5 MiB2024-Jul-08 16:42
42. Quick Tip Correlation Analysis.mp416.9 MiB2024-Jul-08 17:16
10. Quick Tip Clean, Transform, Reduce.mp416.5 MiB2024-Jul-08 17:23
1. Section Overview.mp412.5 MiB2024-Jul-08 17:23
18. Quick Tip How ML Algorithms Work.mp411.1 MiB2024-Jul-08 16:51
7. Typical scikit-learn Workflow.srt31.7 KiB2024-Jul-08 12:39
39. Tuning Hyperparameters.srt30.5 KiB2024-Jul-08 12:32
45. Putting It All Together.srt26.4 KiB2024-Jul-08 16:54
8. Optional Debugging Warnings In Jupyter.srt25.5 KiB2024-Jul-08 17:56
14. Getting Your Data Ready Handling Missing Values With Scikit-learn.srt23.1 KiB2024-Jul-08 16:43
11. Getting Your Data Ready Convert Data To Numbers.srt22.7 KiB2024-Jul-08 18:04
15. Choosing The Right Model For Your Data.srt21.4 KiB2024-Jul-08 13:29
9.1 scikit-learn-data.zip20.8 KiB2024-Jul-08 13:17
41. Tuning Hyperparameters 3.srt18.8 KiB2024-Jul-08 13:02
36. Evaluating A Model With Cross Validation and Scoring Parameter.srt18.0 KiB2024-Jul-08 13:33
25. Evaluating A Machine Learning Model 2 (Cross Validation).srt17.3 KiB2024-Jul-08 17:55
19. Choosing The Right Model For Your Data 3 (Classification).srt17.1 KiB2024-Jul-08 16:49
40. Tuning Hyperparameters 2.srt17.0 KiB2024-Jul-08 17:11
12. Getting Your Data Ready Handling Missing Values With Pandas.srt16.9 KiB2024-Jul-08 13:22
37. Evaluating A Model With Scikit-learn Functions.srt16.3 KiB2024-Jul-08 13:07
46. Putting It All Together 2.srt16.1 KiB2024-Jul-08 13:18
29. Evaluating A Classification Model 4 (Confusion Matrix).srt15.1 KiB2024-Jul-08 17:56
38. Improving A Machine Learning Model.srt14.9 KiB2024-Jul-08 13:33
31. Evaluating A Classification Model 6 (Classification Report).srt14.6 KiB2024-Jul-08 17:14
24. Evaluating A Machine Learning Model (Score).srt12.9 KiB2024-Jul-08 13:43
27. Evaluating A Classification Model 2 (ROC Curve).srt12.3 KiB2024-Jul-08 13:17
9. Getting Your Data Ready Splitting Your Data.srt12.1 KiB2024-Jul-08 13:17
32. Evaluating A Regression Model 1 (R2 Score).srt12.0 KiB2024-Jul-08 16:41
16. Choosing The Right Model For Your Data 2 (Regression).srt12.0 KiB2024-Jul-08 16:51
22. predict() vs predict_proba().srt11.6 KiB2024-Jul-08 13:41
30. Evaluating A Classification Model 5 (Confusion Matrix).srt11.2 KiB2024-Jul-08 13:04
2. Scikit-learn Introduction.srt10.6 KiB2024-Jul-08 12:54
6. Scikit-learn Cheatsheet.srt10.1 KiB2024-Jul-08 12:55
28. Evaluating A Classification Model 3 (ROC Curve).srt10.0 KiB2024-Jul-08 17:31
43. Saving And Loading A Model.srt9.8 KiB2024-Jul-08 12:55
20. Fitting A Model To The Data.srt9.3 KiB2024-Jul-08 17:06
34. Evaluating A Regression Model 3 (MSE).srt9.2 KiB2024-Jul-08 12:58
23. Making Predictions With Our Model (Regression).srt9.1 KiB2024-Jul-08 17:08
44. Saving And Loading A Model 2.srt9.0 KiB2024-Jul-08 12:31
35. Machine Learning Model Evaluation.html7.1 KiB2024-Jul-08 12:58
10. Quick Tip Clean, Transform, Reduce.srt6.4 KiB2024-Jul-08 17:08
4. Refresher What Is Machine Learning.srt6.3 KiB2024-Jul-08 13:32
26. Evaluating A Classification Model 1 (Accuracy).srt5.9 KiB2024-Jul-08 12:52
33. Evaluating A Regression Model 2 (MAE).srt5.7 KiB2024-Jul-08 13:03
1. Section Overview.srt4.1 KiB2024-Jul-08 17:23
42. Quick Tip Correlation Analysis.srt3.1 KiB2024-Jul-08 14:56
13. Note Correction in the upcoming video (splitting data).html2.2 KiB2024-Jul-08 13:22
47. Scikit-Learn Practice.html2.1 KiB2024-Jul-08 13:18
18. Quick Tip How ML Algorithms Work.srt1.9 KiB2024-Jul-08 13:10
5. Quick Note Upcoming Videos.html1018 B2024-Jul-08 13:18
3. Quick Note Upcoming Video.html390 B2024-Jul-08 17:56
17. Quick Note Decision Trees.html221 B2024-Jul-08 16:51
2.2 Introduction to Scikit-Learn Jupyter Notebook (from the upcoming videos).html197 B2024-Jul-08 12:54
46.1 Introduction to Scikit-Learn Jupyter Notebook (from the videos).html197 B2024-Jul-08 13:18
6.1 Scikit-Learn Reference Notebook.html194 B2024-Jul-08 12:55
7.1 Example Scikit-Learn Workflow Notebook.html192 B2024-Jul-08 12:39
2.3 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html191 B2024-Jul-08 12:54
46.2 Introduction to Scikit-Learn Jupyter Notebook (with annotations).html191 B2024-Jul-08 13:18
15.1 Scikit-Learn machine learning map (how to choose the right machine learning model).html133 B2024-Jul-08 13:29
2.1 Scikit-Learn Documentation.html108 B2024-Jul-08 12:54

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