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TORRENT DETAILS

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

TORRENT SUMMARY

	Status:	All the torrents in this section have been verified by our verification system
Category:	Other
Infohash	c36599619801d7fb84e1c3d652da4bb26ca9afcd
Size:	826.3 MB in 84 files
Release Date(s):	Torrent Release: 26-04-2019 by user
Swarm:	0 Seeds & 0 Peers
Vote:	No votes yet.

DESCRIPTION

Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

For More Courses Visit: https://desirecourse.com

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FILE LIST

Filename		Size
	1. Get Started/1. Outline and Motivation.mp4	7.2 MB
	1. Get Started/1. Outline and Motivation.vtt	6 KB
	1. Get Started/2. Where to get the Code and Data.mp4	3.4 MB
	1. Get Started/2. Where to get the Code and Data.vtt	2.6 KB
	1. Get Started/3. All Data is the Same.mp4	5.3 MB
	1. Get Started/3. All Data is the Same.vtt	3.9 KB
	1. Get Started/4. Plug-and-Play.mp4	3.5 MB
	1. Get Started/4. Plug-and-Play.vtt	2.6 KB
	2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.mp4	10.2 MB
	2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.vtt	7.8 KB
	2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.mp4	4.9 MB
	2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.vtt	3.6 KB
	2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.mp4	5.4 MB
	2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.vtt	3.5 KB
	2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp4	41.8 MB
	2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.vtt	11.4 KB
	2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.mp4	13.9 MB
	2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.vtt	5.1 KB
	2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.mp4	7 MB
	2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.vtt	5.1 KB
	3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.mp4	47.7 MB
	3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.vtt	11 KB
	3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.mp4	11 MB
	3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.vtt	3.6 KB
	3. Bootstrap Estimates and Bagging/3. Bagging.mp4	3.9 MB
	3. Bootstrap Estimates and Bagging/3. Bagging.vtt	2.7 KB
	3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.mp4	15.9 MB
	3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.vtt	4 KB
	3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.mp4	20.3 MB
	3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.vtt	4.8 KB
	3. Bootstrap Estimates and Bagging/6. Stacking.mp4	6.1 MB
	3. Bootstrap Estimates and Bagging/6. Stacking.vtt	4.5 KB
	4. Random Forest/1. Random Forest Algorithm.mp4	14.4 MB
	4. Random Forest/1. Random Forest Algorithm.vtt	10.7 KB
	4. Random Forest/2. Random Forest Regressor.mp4	14.9 MB
	4. Random Forest/2. Random Forest Regressor.vtt	7.5 KB
	4. Random Forest/3. Random Forest Classifier.mp4	12.6 MB
	4. Random Forest/3. Random Forest Classifier.vtt	5 KB
	4. Random Forest/4. Random Forest vs Bagging Trees.mp4	7.8 MB
	4. Random Forest/4. Random Forest vs Bagging Trees.vtt	3.9 KB
	4. Random Forest/5. Implementing a Not as Random Forest.mp4	8.7 MB
	4. Random Forest/5. Implementing a Not as Random Forest.vtt	4.4 KB
	4. Random Forest/6. Connection to Deep Learning Dropout.mp4	4.2 MB
	4. Random Forest/6. Connection to Deep Learning Dropout.vtt	2.8 KB
	5. AdaBoost/1. AdaBoost Algorithm.mp4	10.9 MB
	5. AdaBoost/1. AdaBoost Algorithm.vtt	8 KB
	5. AdaBoost/2. Additive Modeling.mp4	2.8 MB
	5. AdaBoost/2. Additive Modeling.vtt	2.1 KB
	5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.mp4	11.2 MB
	5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.vtt	7.4 KB
	5. AdaBoost/4. AdaBoost Implementation.mp4	15.8 MB
	5. AdaBoost/4. AdaBoost Implementation.vtt	9.6 KB
	5. AdaBoost/5. Comparison to Stacking.mp4	5.5 MB
	5. AdaBoost/5. Comparison to Stacking.vtt	3.8 KB
	5. AdaBoost/6. Connection to Deep Learning.mp4	6 MB
	5. AdaBoost/6. Connection to Deep Learning.vtt	4.2 KB
	5. AdaBoost/7. Summary and What's Next.mp4	7.4 MB
	5. AdaBoost/7. Summary and What's Next.vtt	5.5 KB
	6. Appendix/1. What is the Appendix.mp4	5.5 MB
	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.mp4	4 MB
	6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.vtt	3 KB
	6. Appendix/11. Python 2 vs Python 3.mp4	7.8 MB
	6. Appendix/11. Python 2 vs Python 3.vtt	5.4 KB
	6. Appendix/12. What order should I take your courses in (part 1).mp4	29.3 MB
	6. Appendix/12. What order should I take your courses in (part 1).vtt	14.1 KB
	6. Appendix/13. What order should I take your courses in (part 2).mp4	37.6 MB
	6. Appendix/13. What order should I take your courses in (part 2).vtt	20.2 KB
	6. Appendix/2. Confidence Intervals.mp4	12.6 MB
	6. Appendix/2. Confidence Intervals.vtt	11.5 KB
	6. Appendix/3. Windows-Focused Environment Setup 2018.mp4	186.3 MB
	6. Appendix/3. Windows-Focused Environment Setup 2018.vtt	17.4 KB
	6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4	43.9 MB
	6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt	12.4 KB
	6. Appendix/5. How to Code by Yourself (part 1).mp4	24.5 MB
	6. Appendix/5. How to Code by Yourself (part 1).vtt	19.8 KB
	6. Appendix/6. How to Code by Yourself (part 2).mp4	14.8 MB
	6. Appendix/6. How to Code by Yourself (part 2).vtt	11.6 KB
	6. Appendix/7. How to Succeed in this Course (Long Version).mp4	13 MB
	6. Appendix/7. How to Succeed in this Course (Long Version).vtt	12.9 KB
	6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4	39 MB
	6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt	27.8 KB
	6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4	78.3 MB
	6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt	12.2 KB

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