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