Section 1 : Introduction to Unsupervised Learning
Lecture 1 Introduction 00:04:51 Duration
Lecture 2 Course Outline 00:04:24 Duration
Lecture 3 What is unsupervised learning used for 00:05:21 Duration
Lecture 4 Why Use Clustering 00:09:10 Duration
Lecture 5 Where to get the code 00:04:25 Duration
Lecture 6 Anyone Can Succeed in this Course 00:11:46 Duration

Section 2 : K-Means Clustering
Lecture 1 An Easy Introduction to K-Means Clustering 00:06:55 Duration
Lecture 2 Hard K-Means Exercise Prompt 1
Lecture 3 Hard K-Means Exercise 1 Solution 00:10:59 Duration
Lecture 4 Hard K-Means Exercise Prompt 2 00:04:54 Duration
Lecture 5 Hard K-Means Exercise 2 Solution 00:06:59 Duration
Lecture 6 Hard K-Means Exercise Prompt 3 00:06:46 Duration
Lecture 7 Hard K-Means Exercise 3 Solution 00:16:12 Duration
Lecture 8 Hard K-Means Objective Theory 00:12:51 Duration
Lecture 9 Hard K-Means Objective Code 00:05:04 Duration
Lecture 10 Soft K-Means 00:05:33 Duration
Lecture 11 The Soft K-Means Objective Function 00:01:26 Duration
Lecture 12 Soft K-Means in Python Code 00:10:03 Duration
Lecture 13 How to Pace Yourself 00:03:10 Duration
Lecture 14 Visualizing Each Step of K-Means 00:02:18 Duration
Lecture 15 Examples of where K-Means can fail 00:07:32 Duration
Lecture 16 Disadvantages of K-Means Clustering 00:02:03 Duration
Lecture 17 How to Evaluate a Clustering (Purity, Davies-Bouldin Index) 00:06:26 Duration
Lecture 18 Using K-Means on Real Data MNIST 00:05:00 Duration
Lecture 19 One Way to Choose K 00:05:06 Duration
Lecture 20 K-Means Application Finding Clusters of Related Words 00:08:29 Duration
Lecture 21 Clustering for NLP and Computer Vision Real-World Applications 00:06:48 Duration
Lecture 22 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 3 : Hierarchical Clustering
Lecture 1 Visual Walkthrough of Agglomerative Hierarchical Clustering 00:02:21 Duration
Lecture 2 Agglomerative Clustering Options 00:03:30 Duration
Lecture 3 Using Hierarchical Clustering in Python and Interpreting the Dendrogram 00:04:38 Duration
Lecture 4 Application Evolution 00:13:48 Duration
Lecture 5 Application Donald Trump vs

Section 4 : Gaussian Mixture Models (GMMs)
Lecture 1 Gaussian Mixture Model (GMM) Algorithm 00:15:19 Duration
Lecture 2 Write a Gaussian Mixture Model in Python Code
Lecture 3 Practical Issues with GMM Singular Covariance 00:08:57 Duration
Lecture 4 Comparison between GMM and K-Means 00:03:45 Duration
Lecture 5 Kernel Density Estimation 00:06:14 Duration
Lecture 6 GMM vs Bayes Classifier (pt 1) 00:09:17 Duration
Lecture 7 GMM vs Bayes Classifier (pt 2) 00:11:19 Duration
Lecture 8 Expectation-Maximization (pt 1) 00:11:33 Duration
Lecture 9 Expectation-Maximization (pt 2)
Lecture 10 Expectation-Maximization (pt 3)
Lecture 11 Future Unsupervised Learning Algorithms You Will Learn 00:00:50 Duration

Section 5 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 00:20:14 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib 00:17:33 Duration

Section 6 : Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1 How to Code by Yourself part 1 00:15:48 Duration
Lecture 2 How to Code by Yourself part 2 00:09:23 Duration
Lecture 3 Proof that using Jupyter 00:12:24 Duration
Lecture 4 Python 2 vs Python 3 00:04:30 Duration

Section 7 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 1 How to Succeed 00:10:17 Duration
Lecture 2 Is this for Beginners 00:21:58 Duration
Lecture 3 Machine Learning and AI 00:11:13 Duration
Lecture 4 Machine Learning and AI 00:16:07 Duration

Section 8 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:41 Duration