Section 1 : Get Started

 		Lecture 1 Outline and Motivation copy 4:40
Lecture 2 Where to get the Code and Data 1:3
Lecture 3 All Data is the Same 3:15
Lecture 4 Plug-and-Play 2:11

Section 2 : Bias-Variance Trade-Off

 		Lecture 5 Bias-Variance Key Terms 6:37
Lecture 6 Bias-Variance Trade-Off 3:9
Lecture 7 Bias-Variance Decomposition 3:33
Lecture 8 Polynomial Regression Demo 18:8
Lecture 9 K-Nearest Neighbor and Decision Tree Demo 6:32
Lecture 10 Cross-Validation as a Method for Optimizing Model Complexity
Lecture 11 Suggestion Box 2:25

Section 3 : Bootstrap Estimates and Bagging

 		Lecture 12 Bootstrap Estimation 9:55
Lecture 13 Bootstrap Demo 5:20
Lecture 14 Bagging 2:36
Lecture 15 Bagging Regression Trees 7:19
Lecture 16 Bagging Classification Trees 8:39
Lecture 17 Stacking 3:55

Section 4 : Random Forest

 		Lecture 18 Random Forest Algorithm
Lecture 19 Random Forest Regressor 7:5
Lecture 20 Random Forest Classifier 4:56
Lecture 21 Random Forest vs Bagging Trees 3:47
Lecture 22 Implementing a Not as Random Forest 4:13
Lecture 23 Connection to Deep Learning Dropout 2:39

Section 5 : AdaBoost

 		Lecture 24 AdaBoost Algorithm 7:9
Lecture 25 Additive Modeling 1:50
Lecture 26 AdaBoost Loss Function Exponential Loss 7:15
Lecture 27 AdaBoost Implementation 8:26
Lecture 28 Comparison to Stacking 3:29
Lecture 29 Connection to Deep Learning 3:49
Lecture 30 Summary and What's Next 4:55

Section 6 : Background Review

 		Lecture 31 Confidence Intervals 10:12

Section 7 : Setting Up Your Environment (FAQ by Student Request)

 		Lecture 32 Windows-Focused Environment Setup 2018 20:20
Lecture 33 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:33

Section 8 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 		Lecture 34 How to Code by Yourself (part 1) 15:54
Lecture 35 How to Code by Yourself (part 2) 9:23
Lecture 36 Proof that using Jupyter Notebook is the same as not using it 12:29
Lecture 37 Python 2 vs Python 3 4:38

Section 9 : Effective Learning Strategies for Machine Learning )

 		Lecture 38 How to Succeed in this Course (Long Version) 10:26
Lecture 39 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
Lecture 40 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:20
Lecture 41 Machine Learning and AI Prerequisite Roadmap (pt 2)

Section 10 : Appendix FAQ Finale

 		Lecture 42 What is the Appendix