Section 1 : Get Started
Lecture 1 Outline and Motivation copy 00:04:40 Duration
Lecture 2 Where to get the Code and Data 00:01:03 Duration
Lecture 3 All Data is the Same 00:03:15 Duration
Lecture 4 Plug-and-Play 00:02:11 Duration

Section 2 : Bias-Variance Trade-Off
Lecture 1 Bias-Variance Key Terms 00:06:37 Duration
Lecture 2 Bias-Variance Trade-Off 00:03:09 Duration
Lecture 3 Bias-Variance Decomposition 00:03:33 Duration
Lecture 4 Polynomial Regression Demo 00:18:08 Duration
Lecture 5 K-Nearest Neighbor and Decision Tree Demo 00:06:32 Duration
Lecture 6 Cross-Validation as a Method for Optimizing Model Complexity
Lecture 7 Suggestion Box 00:02:25 Duration

Section 3 : Bootstrap Estimates and Bagging
Lecture 1 Bootstrap Estimation 00:09:55 Duration
Lecture 2 Bootstrap Demo 00:05:20 Duration
Lecture 3 Bagging 00:02:36 Duration
Lecture 4 Bagging Regression Trees 00:07:19 Duration
Lecture 5 Bagging Classification Trees 00:08:39 Duration
Lecture 6 Stacking 00:03:55 Duration

Section 4 : Random Forest
Lecture 1 Random Forest Algorithm
Lecture 2 Random Forest Regressor 00:07:05 Duration
Lecture 3 Random Forest Classifier 00:04:56 Duration
Lecture 4 Random Forest vs Bagging Trees 00:03:47 Duration
Lecture 5 Implementing a Not as Random Forest 00:04:13 Duration
Lecture 6 Connection to Deep Learning Dropout 00:02:39 Duration

Section 5 : AdaBoost
Lecture 1 AdaBoost Algorithm 00:07:09 Duration
Lecture 2 Additive Modeling 00:01:50 Duration
Lecture 3 AdaBoost Loss Function Exponential Loss 00:07:15 Duration
Lecture 4 AdaBoost Implementation 00:08:26 Duration
Lecture 5 Comparison to Stacking 00:03:29 Duration
Lecture 6 Connection to Deep Learning 00:03:49 Duration
Lecture 7 Summary and What's Next 00:04:55 Duration

Section 6 : Background Review
Lecture 1 Confidence Intervals 00:10:12 Duration

Section 7 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 2018 00:20:20 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:33 Duration

Section 8 : Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1 How to Code by Yourself (part 1) 00:15:54 Duration
Lecture 2 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 3 Proof that using Jupyter Notebook is the same as not using it 00:12:29 Duration
Lecture 4 Python 2 vs Python 3 00:04:38 Duration

Section 9 : Effective Learning Strategies for Machine Learning )
Lecture 1 How to Succeed in this Course (Long Version) 00:10:26 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1) 00:11:20 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2)

Section 10 : Appendix FAQ Finale
Lecture 1 What is the Appendix