[FreeCourseLab.me] Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost - 磁力管家

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文档列表

6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.mp4 50.0 MB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.1 MB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp4 43.8 MB
6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
6. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.srt 39.7 MB
6. Appendix FAQ/13. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
6. Appendix FAQ/12. What order should I take your courses in (part 2).mp4 39.5 MB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.srt 31.5 MB
6. Appendix FAQ/11. What order should I take your courses in (part 1).mp4 30.8 MB
6. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.mp4 21.3 MB
4. Random Forest/6. Connection to Deep Learning Dropout.srt 19.9 MB
5. AdaBoost/4. AdaBoost Implementation.srt 19.4 MB
4. Random Forest/1. Random Forest Algorithm.srt 17.7 MB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.mp4 16.6 MB
5. AdaBoost/4. AdaBoost Implementation.mp4 16.5 MB
4. Random Forest/2. Random Forest Regressor.mp4 15.6 MB
6. Appendix FAQ/6. How to Code by Yourself (part 2).mp4 15.5 MB
4. Random Forest/1. Random Forest Algorithm.mp4 15.1 MB
2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.mp4 15.0 MB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.srt 14.7 MB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.srt 14.5 MB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.mp4 14.5 MB
6. Appendix FAQ/7. How to Succeed in this Course (Long Version).mp4 13.6 MB
6. Appendix FAQ/2. Confidence Intervals.mp4 13.2 MB
4. Random Forest/3. Random Forest Classifier.mp4 13.2 MB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.mp4 11.7 MB
3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.mp4 11.5 MB
5. AdaBoost/1. AdaBoost Algorithm.mp4 11.4 MB
2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.mp4 10.7 MB
4. Random Forest/5. Implementing a Not as Random Forest.mp4 9.1 MB
6. Appendix FAQ/10. Python 2 vs Python 3.mp4 8.2 MB
4. Random Forest/4. Random Forest vs Bagging Trees.mp4 8.2 MB
5. AdaBoost/7. Summary and What's Next.mp4 7.7 MB
1. Get Started/1. Outline and Motivation.mp4 7.5 MB
2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.mp4 7.3 MB
3. Bootstrap Estimates and Bagging/6. Stacking.mp4 6.4 MB
5. AdaBoost/6. Connection to Deep Learning.mp4 6.3 MB
6. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
5. AdaBoost/5. Comparison to Stacking.mp4 5.7 MB
6. Appendix FAQ/7. How to Succeed in this Course (Long Version).srt 5.6 MB
1. Get Started/3. All Data is the Same.mp4 5.5 MB
2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.mp4 5.1 MB
4. Random Forest/6. Connection to Deep Learning Dropout.mp4 4.4 MB
3. Bootstrap Estimates and Bagging/3. Bagging.srt 4.1 MB
3. Bootstrap Estimates and Bagging/3. Bagging.mp4 4.1 MB
1. Get Started/4. Plug-and-Play.mp4 3.7 MB
1. Get Started/2. Where to get the Code and Data.mp4 3.5 MB
5. AdaBoost/2. Additive Modeling.mp4 2.9 MB
6. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.5 kB
6. Appendix FAQ/12. What order should I take your courses in (part 2).srt 23.6 kB
6. Appendix FAQ/5. How to Code by Yourself (part 1).srt 23.3 kB
6. Appendix FAQ/3. Windows-Focused Environment Setup 2018.srt 20.6 kB
6. Appendix FAQ/11. What order should I take your courses in (part 1).srt 16.4 kB
6. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.8 kB
6. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.srt 13.6 kB
6. Appendix FAQ/6. How to Code by Yourself (part 2).srt 13.6 kB
6. Appendix FAQ/2. Confidence Intervals.srt 13.5 kB
5. AdaBoost/1. AdaBoost Algorithm.srt 9.2 kB
2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.srt 9.1 kB
4. Random Forest/2. Random Forest Regressor.srt 8.7 kB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.srt 8.6 kB
1. Get Started/1. Outline and Motivation.srt 7.0 kB
5. AdaBoost/7. Summary and What's Next.srt 6.3 kB
6. Appendix FAQ/10. Python 2 vs Python 3.srt 6.2 kB
2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.srt 5.9 kB
4. Random Forest/3. Random Forest Classifier.srt 5.8 kB
3. Bootstrap Estimates and Bagging/6. Stacking.srt 5.2 kB
4. Random Forest/5. Implementing a Not as Random Forest.srt 5.1 kB
5. AdaBoost/6. Connection to Deep Learning.srt 4.9 kB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.srt 4.9 kB
1. Get Started/3. All Data is the Same.srt 4.5 kB
4. Random Forest/4. Random Forest vs Bagging Trees.srt 4.5 kB
5. AdaBoost/5. Comparison to Stacking.srt 4.4 kB
3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.srt 4.3 kB
2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.srt 4.2 kB
6. Appendix FAQ/1. What is the Appendix.srt 3.8 kB
2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.srt 3.8 kB
1. Get Started/4. Plug-and-Play.srt 3.0 kB
1. Get Started/2. Where to get the Code and Data.srt 2.9 kB
5. AdaBoost/2. Additive Modeling.srt 2.4 kB
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