#### Section 1 : Introduction to Unsupervised Learning

 Lecture 1 Introduction 4:51 Lecture 2 Course Outline 4:24 Lecture 3 What is unsupervised learning used for 5:21 Lecture 4 Why Use Clustering 9:10 Lecture 5 Where to get the code 4:25 Lecture 6 Anyone Can Succeed in this Course 11:46

#### Section 2 : K-Means Clustering

 Lecture 7 An Easy Introduction to K-Means Clustering 6:55 Lecture 8 Hard K-Means Exercise Prompt 1 Lecture 9 Hard K-Means Exercise 1 Solution 10:59 Lecture 10 Hard K-Means Exercise Prompt 2 4:54 Lecture 11 Hard K-Means Exercise 2 Solution 6:59 Lecture 12 Hard K-Means Exercise Prompt 3 6:46 Lecture 13 Hard K-Means Exercise 3 Solution 16:12 Lecture 14 Hard K-Means Objective Theory 12:51 Lecture 15 Hard K-Means Objective Code 5:4 Lecture 16 Soft K-Means 5:33 Lecture 17 The Soft K-Means Objective Function 1:26 Lecture 18 Soft K-Means in Python Code 10:3 Lecture 19 How to Pace Yourself 3:10 Lecture 20 Visualizing Each Step of K-Means 2:18 Lecture 21 Examples of where K-Means can fail 7:32 Lecture 22 Disadvantages of K-Means Clustering 2:3 Lecture 23 How to Evaluate a Clustering (Purity, Davies-Bouldin Index) 6:26 Lecture 24 Using K-Means on Real Data MNIST 5:0 Lecture 25 One Way to Choose K 5:6 Lecture 26 K-Means Application Finding Clusters of Related Words 8:29 Lecture 27 Clustering for NLP and Computer Vision Real-World Applications 6:48 Lecture 28 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

#### Section 3 : Hierarchical Clustering

 Lecture 29 Visual Walkthrough of Agglomerative Hierarchical Clustering 2:21 Lecture 30 Agglomerative Clustering Options 3:30 Lecture 31 Using Hierarchical Clustering in Python and Interpreting the Dendrogram 4:38 Lecture 32 Application Evolution 13:48 Lecture 33 Application Donald Trump vs

#### Section 4 : Gaussian Mixture Models (GMMs)

 Lecture 34 Gaussian Mixture Model (GMM) Algorithm 15:19 Lecture 35 Write a Gaussian Mixture Model in Python Code Lecture 36 Practical Issues with GMM Singular Covariance 8:57 Lecture 37 Comparison between GMM and K-Means 3:45 Lecture 38 Kernel Density Estimation 6:14 Lecture 39 GMM vs Bayes Classifier (pt 1) 9:17 Lecture 40 GMM vs Bayes Classifier (pt 2) 11:19 Lecture 41 Expectation-Maximization (pt 1) 11:33 Lecture 42 Expectation-Maximization (pt 2) Lecture 43 Expectation-Maximization (pt 3) Lecture 44 Future Unsupervised Learning Algorithms You Will Learn 0:50

#### Section 5 : Setting Up Your Environment (FAQ by Student Request)

 Lecture 45 Windows-Focused Environment Setup 20:14 Lecture 46 How to install Numpy, Scipy, Matplotlib 17:33

#### Section 6 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 Lecture 47 How to Code by Yourself part 1 15:48 Lecture 48 How to Code by Yourself part 2 9:23 Lecture 49 Proof that using Jupyter 12:24 Lecture 50 Python 2 vs Python 3 4:30

#### Section 7 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

 Lecture 51 How to Succeed 10:17 Lecture 52 Is this for Beginners 21:58 Lecture 53 Machine Learning and AI 11:13 Lecture 54 Machine Learning and AI 16:7

#### Section 8 : Appendix FAQ Finale

 Lecture 55 What is the Appendix 2:41