#### 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

 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