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

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

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种子哈希：c36599619801d7fb84e1c3d652da4bb26ca9afcd
文件大小：826.29M
已经下载：730次
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收录时间：2021-03-15
最近下载：2025-06-02

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文件列表

  • 6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 195.3 MB
  • 6. Appendix/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/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
  • 2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp4 43.8 MB
  • 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.8 MB
  • 6. Appendix/13. What order should I take your courses in (part 2).mp4 39.4 MB
  • 6. Appendix/12. What order should I take your courses in (part 1).mp4 30.7 MB
  • 6. Appendix/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
  • 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/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/5. K-Nearest Neighbor and Decision Tree Demo.mp4 14.5 MB
  • 6. Appendix/7. How to Succeed in this Course (Long Version).mp4 13.6 MB
  • 6. Appendix/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/11. 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/1. What is the Appendix.mp4 5.7 MB
  • 5. AdaBoost/5. Comparison to Stacking.mp4 5.7 MB
  • 2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.mp4 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
  • 6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4.2 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/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28.4 kB
  • 6. Appendix/13. What order should I take your courses in (part 2).vtt 20.7 kB
  • 6. Appendix/5. How to Code by Yourself (part 1).vtt 20.3 kB
  • 6. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17.8 kB
  • 6. Appendix/12. What order should I take your courses in (part 1).vtt 14.4 kB
  • 6. Appendix/7. How to Succeed in this Course (Long Version).vtt 13.2 kB
  • 6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.7 kB
  • 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12.5 kB
  • 6. Appendix/6. How to Code by Yourself (part 2).vtt 11.9 kB
  • 6. Appendix/2. Confidence Intervals.vtt 11.8 kB
  • 2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.vtt 11.7 kB
  • 3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.vtt 11.3 kB
  • 4. Random Forest/1. Random Forest Algorithm.vtt 11.0 kB
  • 5. AdaBoost/4. AdaBoost Implementation.vtt 9.8 kB
  • 5. AdaBoost/1. AdaBoost Algorithm.vtt 8.2 kB
  • 2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.vtt 8.0 kB
  • 4. Random Forest/2. Random Forest Regressor.vtt 7.6 kB
  • 5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.vtt 7.6 kB
  • 1. Get Started/1. Outline and Motivation.vtt 6.2 kB
  • 5. AdaBoost/7. Summary and What's Next.vtt 5.6 kB
  • 6. Appendix/11. Python 2 vs Python 3.vtt 5.5 kB
  • 2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.vtt 5.2 kB
  • 2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.vtt 5.2 kB
  • 4. Random Forest/3. Random Forest Classifier.vtt 5.1 kB
  • 3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.vtt 4.9 kB
  • 3. Bootstrap Estimates and Bagging/6. Stacking.vtt 4.6 kB
  • 4. Random Forest/5. Implementing a Not as Random Forest.vtt 4.5 kB
  • 5. AdaBoost/6. Connection to Deep Learning.vtt 4.3 kB
  • 3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.vtt 4.1 kB
  • 1. Get Started/3. All Data is the Same.vtt 4.0 kB
  • 4. Random Forest/4. Random Forest vs Bagging Trees.vtt 4.0 kB
  • 5. AdaBoost/5. Comparison to Stacking.vtt 3.9 kB
  • 3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.vtt 3.7 kB
  • 2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.vtt 3.7 kB
  • 2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.vtt 3.6 kB
  • 6. Appendix/1. What is the Appendix.vtt 3.4 kB
  • 6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.vtt 3.1 kB
  • 4. Random Forest/6. Connection to Deep Learning Dropout.vtt 2.9 kB
  • 3. Bootstrap Estimates and Bagging/3. Bagging.vtt 2.8 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
  • [DesireCourse.Com].url 51 Bytes

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