Udemy - Ensemble Machine Learning in Python Random Forest, AdaBoost (10.2021)
File List
- 07 - Setting Up Your Environment (FAQ by Student Request)/001 Anaconda Environment Setup.mp4 168.0 MB
- 02 - Bias-Variance Trade-Off/004 Polynomial Regression Demo.mp4 143.1 MB
- 07 - Setting Up Your Environment (FAQ by Student Request)/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 109.3 MB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/004 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 81.2 MB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/003 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 75.7 MB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/003 Proof that using Jupyter Notebook is the same as not using it.mp4 64.3 MB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/001 How to Code by Yourself (part 1).mp4 56.1 MB
- 01 - Get Started/002 Where to get the Code and Data.mp4 51.7 MB
- 04 - Random Forest/002 Random Forest Regressor.mp4 48.0 MB
- 01 - Get Started/005 How to Succeed in This Course.mp4 43.9 MB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.mp4 42.5 MB
- 10 - Appendix _ FAQ Finale/002 BONUS.mp4 35.7 MB
- 04 - Random Forest/003 Random Forest Classifier.mp4 33.3 MB
- 01 - Get Started/001 Outline and Motivation.mp4 32.4 MB
- 03 - Bootstrap Estimates and Bagging/001 Bootstrap Estimation.mp4 29.7 MB
- 05 - AdaBoost/004 AdaBoost Implementation.mp4 29.0 MB
- 03 - Bootstrap Estimates and Bagging/005 Bagging Classification Trees.mp4 28.4 MB
- 05 - AdaBoost/003 AdaBoost Loss Function_ Exponential Loss.mp4 23.4 MB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/002 How to Code by Yourself (part 2).mp4 20.9 MB
- 06 - Background Review/001 Confidence Intervals.mp4 20.7 MB
- 04 - Random Forest/001 Random Forest Algorithm.mp4 20.6 MB
- 02 - Bias-Variance Trade-Off/005 K-Nearest Neighbor and Decision Tree Demo.mp4 20.0 MB
- 03 - Bootstrap Estimates and Bagging/004 Bagging Regression Trees.mp4 18.6 MB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/001 How to Succeed in this Course (Long Version).mp4 17.9 MB
- 05 - AdaBoost/001 AdaBoost Algorithm.mp4 16.4 MB
- 02 - Bias-Variance Trade-Off/001 Bias-Variance Key Terms.mp4 14.8 MB
- 04 - Random Forest/005 Implementing a _Not as Random_ Forest.mp4 13.8 MB
- 03 - Bootstrap Estimates and Bagging/002 Bootstrap Demo.mp4 12.8 MB
- 02 - Bias-Variance Trade-Off/007 Suggestion Box.mp4 11.8 MB
- 01 - Get Started/004 Plug-and-Play.mp4 9.7 MB
- 04 - Random Forest/004 Random Forest vs Bagging Trees.mp4 9.7 MB
- 05 - AdaBoost/007 Summary and What's Next.mp4 9.7 MB
- 02 - Bias-Variance Trade-Off/006 Cross-Validation as a Method for Optimizing Model Complexity.mp4 9.4 MB
- 05 - AdaBoost/006 Connection to Deep Learning.mp4 9.1 MB
- 03 - Bootstrap Estimates and Bagging/006 Stacking.mp4 8.9 MB
- 03 - Bootstrap Estimates and Bagging/003 Bagging.mp4 8.7 MB
- 01 - Get Started/003 All Data is the Same.mp4 8.0 MB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/004 Python 2 vs Python 3.mp4 7.6 MB
- 05 - AdaBoost/005 Comparison to Stacking.mp4 7.5 MB
- 02 - Bias-Variance Trade-Off/002 Bias-Variance Trade-Off.mp4 6.2 MB
- 10 - Appendix _ FAQ Finale/001 What is the Appendix_.mp4 6.1 MB
- 04 - Random Forest/006 Connection to Deep Learning_ Dropout.mp4 5.5 MB
- 02 - Bias-Variance Trade-Off/003 Bias-Variance Decomposition.mp4 5.2 MB
- 05 - AdaBoost/002 Additive Modeling.mp4 3.4 MB
- 01 - Get Started/2021 Software - Windows - Office - Adobe.pdf 225.8 KB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/002 Is this for Beginners or Experts_ Academic or Practical_ Fast or slow-paced_.vtt 27.8 KB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/004 Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 20.2 KB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/001 How to Code by Yourself (part 1).vtt 19.8 KB
- 07 - Setting Up Your Environment (FAQ by Student Request)/001 Anaconda Environment Setup.vtt 17.4 KB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/003 Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 14.1 KB
- 09 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/001 How to Succeed in this Course (Long Version).vtt 12.9 KB
- 07 - Setting Up Your Environment (FAQ by Student Request)/002 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.4 KB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/003 Proof that using Jupyter Notebook is the same as not using it.vtt 12.2 KB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/002 How to Code by Yourself (part 2).vtt 11.6 KB
- 01 - Get Started/002 Where to get the Code and Data.vtt 11.6 KB
- 06 - Background Review/001 Confidence Intervals.vtt 11.5 KB
- 02 - Bias-Variance Trade-Off/004 Polynomial Regression Demo.vtt 11.4 KB
- 03 - Bootstrap Estimates and Bagging/001 Bootstrap Estimation.vtt 11.0 KB
- 04 - Random Forest/001 Random Forest Algorithm.vtt 10.7 KB
- 05 - AdaBoost/004 AdaBoost Implementation.vtt 9.6 KB
- 05 - AdaBoost/001 AdaBoost Algorithm.vtt 8.0 KB
- 02 - Bias-Variance Trade-Off/001 Bias-Variance Key Terms.vtt 7.8 KB
- 04 - Random Forest/002 Random Forest Regressor.vtt 7.5 KB
- 05 - AdaBoost/003 AdaBoost Loss Function_ Exponential Loss.vtt 7.4 KB
- 01 - Get Started/005 How to Succeed in This Course.vtt 7.3 KB
- 01 - Get Started/001 Outline and Motivation.vtt 7.0 KB
- 10 - Appendix _ FAQ Finale/002 BONUS.vtt 6.9 KB
- 05 - AdaBoost/007 Summary and What's Next.vtt 5.5 KB
- 08 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/004 Python 2 vs Python 3.vtt 5.4 KB
- 02 - Bias-Variance Trade-Off/006 Cross-Validation as a Method for Optimizing Model Complexity.vtt 5.1 KB
- 02 - Bias-Variance Trade-Off/005 K-Nearest Neighbor and Decision Tree Demo.vtt 5.1 KB
- 04 - Random Forest/003 Random Forest Classifier.vtt 5.0 KB
- 03 - Bootstrap Estimates and Bagging/005 Bagging Classification Trees.vtt 4.8 KB
- 03 - Bootstrap Estimates and Bagging/006 Stacking.vtt 4.5 KB
- 04 - Random Forest/005 Implementing a _Not as Random_ Forest.vtt 4.4 KB
- 05 - AdaBoost/006 Connection to Deep Learning.vtt 4.2 KB
- 02 - Bias-Variance Trade-Off/007 Suggestion Box.vtt 4.1 KB
- 03 - Bootstrap Estimates and Bagging/004 Bagging Regression Trees.vtt 4.0 KB
- 01 - Get Started/003 All Data is the Same.vtt 3.9 KB
- 04 - Random Forest/004 Random Forest vs Bagging Trees.vtt 3.9 KB
- 05 - AdaBoost/005 Comparison to Stacking.vtt 3.8 KB
- 03 - Bootstrap Estimates and Bagging/002 Bootstrap Demo.vtt 3.6 KB
- 02 - Bias-Variance Trade-Off/002 Bias-Variance Trade-Off.vtt 3.6 KB
- 02 - Bias-Variance Trade-Off/003 Bias-Variance Decomposition.vtt 3.3 KB
- 10 - Appendix _ FAQ Finale/001 What is the Appendix_.vtt 3.3 KB
- 04 - Random Forest/006 Connection to Deep Learning_ Dropout.vtt 2.8 KB
- 03 - Bootstrap Estimates and Bagging/003 Bagging.vtt 2.8 KB
- 01 - Get Started/004 Plug-and-Play.vtt 2.6 KB
- 05 - AdaBoost/002 Additive Modeling.vtt 2.1 KB
- 01 - Get Started/external-assets-links.txt 76 bytes
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