[DesireCourse.Com] Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost

Creation Time	July 12, 2025, 4:58 p.m.
Last Access Time	July 12, 2025, 4:58 p.m.
File Size	826.3 MB
Keywords	Python DesireCourse Random - Udemy Machine Forest Learning in Com Ensemble AdaBoost
Total Requests	1
Total Files	85
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6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186.3 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.mp4 47.7 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp4 41.8 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
6. Appendix/13. What order should I take your courses in (part 2).mp4 37.6 MB
6. Appendix/12. What order should I take your courses in (part 1).mp4 29.3 MB
6. Appendix/5. How to Code by Yourself (part 1).mp4 24.5 MB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.mp4 20.3 MB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.mp4 15.9 MB
5. AdaBoost/4. AdaBoost Implementation.mp4 15.8 MB
4. Random Forest/2. Random Forest Regressor.mp4 14.9 MB
6. Appendix/6. How to Code by Yourself (part 2).mp4 14.8 MB
4. Random Forest/1. Random Forest Algorithm.mp4 14.4 MB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.mp4 13.9 MB
6. Appendix/7. How to Succeed in this Course (Long Version).mp4 13.0 MB
6. Appendix/2. Confidence Intervals.mp4 12.6 MB
4. Random Forest/3. Random Forest Classifier.mp4 12.6 MB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.mp4 11.2 MB
3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.mp4 11.0 MB
5. AdaBoost/1. AdaBoost Algorithm.mp4 10.9 MB
2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.mp4 10.2 MB
4. Random Forest/5. Implementing a Not as Random Forest.mp4 8.7 MB
6. Appendix/11. Python 2 vs Python 3.mp4 7.8 MB
4. Random Forest/4. Random Forest vs Bagging Trees.mp4 7.8 MB
5. AdaBoost/7. Summary and What's Next.mp4 7.4 MB
1. Get Started/1. Outline and Motivation.mp4 7.2 MB
2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.mp4 7.0 MB
3. Bootstrap Estimates and Bagging/6. Stacking.mp4 6.1 MB
5. AdaBoost/6. Connection to Deep Learning.mp4 6.0 MB
6. Appendix/1. What is the Appendix.mp4 5.5 MB
5. AdaBoost/5. Comparison to Stacking.mp4 5.5 MB
2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.mp4 5.4 MB
1. Get Started/3. All Data is the Same.mp4 5.3 MB
2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.mp4 4.9 MB
4. Random Forest/6. Connection to Deep Learning Dropout.mp4 4.2 MB
6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4.0 MB
3. Bootstrap Estimates and Bagging/3. Bagging.mp4 3.9 MB
1. Get Started/4. Plug-and-Play.mp4 3.5 MB
1. Get Started/2. Where to get the Code and Data.mp4 3.4 MB
5. AdaBoost/2. Additive Modeling.mp4 2.8 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.8 KB
6. Appendix/13. What order should I take your courses in (part 2).vtt 20.2 KB
6. Appendix/5. How to Code by Yourself (part 1).vtt 19.8 KB
6. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17.4 KB
6. Appendix/12. What order should I take your courses in (part 1).vtt 14.1 KB
6. Appendix/7. How to Succeed in this Course (Long Version).vtt 12.9 KB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.4 KB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12.2 KB
6. Appendix/6. How to Code by Yourself (part 2).vtt 11.6 KB
6. Appendix/2. Confidence Intervals.vtt 11.5 KB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.vtt 11.4 KB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.vtt 11.0 KB
4. Random Forest/1. Random Forest Algorithm.vtt 10.7 KB
5. AdaBoost/4. AdaBoost Implementation.vtt 9.6 KB
5. AdaBoost/1. AdaBoost Algorithm.vtt 8.0 KB
2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.vtt 7.8 KB
4. Random Forest/2. Random Forest Regressor.vtt 7.5 KB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.vtt 7.4 KB
1. Get Started/1. Outline and Motivation.vtt 6.0 KB
5. AdaBoost/7. Summary and What's Next.vtt 5.5 KB
6. Appendix/11. Python 2 vs Python 3.vtt 5.4 KB
2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.vtt 5.1 KB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.vtt 5.1 KB
4. Random Forest/3. Random Forest Classifier.vtt 5.0 KB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.vtt 4.8 KB
3. Bootstrap Estimates and Bagging/6. Stacking.vtt 4.5 KB
4. Random Forest/5. Implementing a Not as Random Forest.vtt 4.4 KB
5. AdaBoost/6. Connection to Deep Learning.vtt 4.2 KB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.vtt 4.0 KB
1. Get Started/3. All Data is the Same.vtt 3.9 KB
4. Random Forest/4. Random Forest vs Bagging Trees.vtt 3.9 KB
5. AdaBoost/5. Comparison to Stacking.vtt 3.8 KB
3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.vtt 3.6 KB
2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.vtt 3.6 KB
2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.vtt 3.5 KB
6. Appendix/1. What is the Appendix.vtt 3.3 KB
6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.vtt 3.0 KB
4. Random Forest/6. Connection to Deep Learning Dropout.vtt 2.8 KB
3. Bootstrap Estimates and Bagging/3. Bagging.vtt 2.7 KB
1. Get Started/4. Plug-and-Play.vtt 2.6 KB
1. Get Started/2. Where to get the Code and Data.vtt 2.6 KB
5. AdaBoost/2. Additive Modeling.vtt 2.1 KB
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[DesireCourse.Com] Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost

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