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

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Title: UdemyGroup: NOGRPSource: Udemy
Info Hash
C36599619801D7FB84E1C3D652DA4BB26CA9AFCD
Source
Unverified
Total Size
826.29 MB
Total Files
85
Seeders
1
Leechers
0
Health
1.00
Score
2
Type
Bookware

File List

FileSize
1. Get Started/1. Outline and Motivation.mp47.2 MB
1. Get Started/1. Outline and Motivation.vtt6.02 KB
1. Get Started/2. Where to get the Code and Data.mp43.36 MB
1. Get Started/2. Where to get the Code and Data.vtt2.55 KB
1. Get Started/3. All Data is the Same.mp45.25 MB
1. Get Started/3. All Data is the Same.vtt3.94 KB
1. Get Started/4. Plug-and-Play.mp43.51 MB
1. Get Started/4. Plug-and-Play.vtt2.58 KB
2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.mp410.23 MB
2. Bias-Variance Trade-Off/1. Bias-Variance Key Terms.vtt7.84 KB
2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.mp44.89 MB
2. Bias-Variance Trade-Off/2. Bias-Variance Trade-Off.vtt3.57 KB
2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.mp45.37 MB
2. Bias-Variance Trade-Off/3. Bias-Variance Decomposition.vtt3.51 KB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.mp441.75 MB
2. Bias-Variance Trade-Off/4. Polynomial Regression Demo.vtt11.45 KB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.mp413.86 MB
2. Bias-Variance Trade-Off/5. K-Nearest Neighbor and Decision Tree Demo.vtt5.06 KB
2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.mp46.97 MB
2. Bias-Variance Trade-Off/6. Cross-Validation as a Method for Optimizing Model Complexity.vtt5.1 KB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.mp447.72 MB
3. Bootstrap Estimates and Bagging/1. Bootstrap Estimation.vtt11.03 KB
3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.mp410.95 MB
3. Bootstrap Estimates and Bagging/2. Bootstrap Demo.vtt3.62 KB
3. Bootstrap Estimates and Bagging/3. Bagging.mp43.93 MB
3. Bootstrap Estimates and Bagging/3. Bagging.vtt2.7 KB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.mp415.87 MB
3. Bootstrap Estimates and Bagging/4. Bagging Regression Trees.vtt4.04 KB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.mp420.32 MB
3. Bootstrap Estimates and Bagging/5. Bagging Classification Trees.vtt4.76 KB
3. Bootstrap Estimates and Bagging/6. Stacking.mp46.07 MB
3. Bootstrap Estimates and Bagging/6. Stacking.vtt4.47 KB
4. Random Forest/1. Random Forest Algorithm.mp414.42 MB
4. Random Forest/1. Random Forest Algorithm.vtt10.71 KB
4. Random Forest/2. Random Forest Regressor.mp414.89 MB
4. Random Forest/2. Random Forest Regressor.vtt7.47 KB
4. Random Forest/3. Random Forest Classifier.mp412.59 MB
4. Random Forest/3. Random Forest Classifier.vtt5 KB
4. Random Forest/4. Random Forest vs Bagging Trees.mp47.82 MB
4. Random Forest/4. Random Forest vs Bagging Trees.vtt3.9 KB
4. Random Forest/5. Implementing a Not as Random Forest.mp48.69 MB
4. Random Forest/5. Implementing a Not as Random Forest.vtt4.42 KB
4. Random Forest/6. Connection to Deep Learning Dropout.mp44.22 MB
4. Random Forest/6. Connection to Deep Learning Dropout.vtt2.85 KB
5. AdaBoost/1. AdaBoost Algorithm.mp410.89 MB
5. AdaBoost/1. AdaBoost Algorithm.vtt7.96 KB
5. AdaBoost/2. Additive Modeling.mp42.8 MB
5. AdaBoost/2. Additive Modeling.vtt2.07 KB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.mp411.18 MB
5. AdaBoost/3. AdaBoost Loss Function Exponential Loss.vtt7.44 KB
5. AdaBoost/4. AdaBoost Implementation.mp415.78 MB
5. AdaBoost/4. AdaBoost Implementation.vtt9.56 KB
5. AdaBoost/5. Comparison to Stacking.mp45.45 MB
5. AdaBoost/5. Comparison to Stacking.vtt3.85 KB
5. AdaBoost/6. Connection to Deep Learning.mp46.03 MB
5. AdaBoost/6. Connection to Deep Learning.vtt4.22 KB
5. AdaBoost/7. Summary and What's Next.mp47.37 MB
5. AdaBoost/7. Summary and What's Next.vtt5.47 KB
6. Appendix/1. What is the Appendix.mp45.45 MB
6. Appendix/1. What is the Appendix.vtt3.28 KB
6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.02 MB
6. Appendix/10. BONUS Where to get Udemy coupons and FREE deep learning material.vtt2.99 KB
6. Appendix/11. Python 2 vs Python 3.mp47.83 MB
6. Appendix/11. Python 2 vs Python 3.vtt5.35 KB
6. Appendix/12. What order should I take your courses in (part 1).mp429.32 MB
6. Appendix/12. What order should I take your courses in (part 1).vtt14.09 KB
6. Appendix/13. What order should I take your courses in (part 2).mp437.62 MB
6. Appendix/13. What order should I take your courses in (part 2).vtt20.24 KB
6. Appendix/2. Confidence Intervals.mp412.59 MB
6. Appendix/2. Confidence Intervals.vtt11.55 KB
6. Appendix/3. Windows-Focused Environment Setup 2018.mp4186.29 MB
6. Appendix/3. Windows-Focused Environment Setup 2018.vtt17.39 KB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92 MB
6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt12.4 KB
6. Appendix/5. How to Code by Yourself (part 1).mp424.53 MB
6. Appendix/5. How to Code by Yourself (part 1).vtt19.78 KB
6. Appendix/6. How to Code by Yourself (part 2).mp414.81 MB
6. Appendix/6. How to Code by Yourself (part 2).vtt11.62 KB
6. Appendix/7. How to Succeed in this Course (Long Version).mp413 MB
6. Appendix/7. How to Succeed in this Course (Long Version).vtt12.86 KB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95 MB
6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt27.77 KB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp478.26 MB
6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt12.22 KB
[DesireCourse.Com].url51 B

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