My name is Peter Chen and I am the instructor for this course. I want to introduce you to the wonderful world of Unsupervised Machine Learning. Specifically, we will focus on Clustering algorithms and methods through practical examples and code. More importantly, it will get you up and running quickly with a clear conceptual understanding. The course has code & sample data for you to run and learn from. It also encourages you to explore your own datasets using Clustering algorithms.
Beginner knowledge of Python. It's used mostly for expository reasons. You do not need to be a Python expert. Basic math and comfortable with basic probability and statistics.
What am I going to get from this course?
* Understand the major types of clustering algorithms
* Know what, how, when to apply a k-means, GMM, and hierarchical clustering
* Understand the power of Gaussian Mixture Models(GMM) to go beyond simple clustering needs
* Determine the optimal number of clusters
* Gained an intuition behind the math of the underlying algorithms and be able to explain it
* Learn how to use Python scikit-learn library to build clustering machine learning models
* Apply Python code to their data sets to solve clustering various problems
* Evaluate the quality of clustering using Silhouette plots
* Learn about different industry applications of Clustering
Prerequisites and Target Audience
What will students need to know or do before starting this course?
Basic Python. Do not need to be an expert programmer. We use Python mainly for expository reasons. Basic probability math.
Who should take this course? Who should not?
Students who are interested in a practical introduction to clustering, a kind of unsupervised machine learning. Want an intuitive understanding of the theory behind clustering.
Students can use these methods and algorithms for hot applications such as marketing analytics, customer segmentation, anomaly detection, fraud detection, and other practical applications in their respective fields. Must like to play with data and code.
Module 1: Welcome & Introductions
Welcome to the Course
Course Overview and Introductions
Module 2: K-Means Clustering
How does K-means do that?
Issues with K-Means
Module 3: Gaussian Mixture Models
GMM: Code Examples
GMM as Density Estimators
GMM: Optimal Number of Components
GMM - Generate New Data
Module 4: Hierarchical Clustering
Introductions to Hierarchical Clustering
Hierarchical Clustering Walk-Through
Hierarchical Clustering - Code Examples
Module 5: Methods for Selecting Number of Clusters
Methods for Selecting Number of Clusters
Module 6: Evaluating the Quality of the Clustering
Evaluating the Quality of Clustering
Module 7: Industry Applications
Module 8: Mini-Project: Pulling It All Together
Module 9: Mini-Project Solution Preview