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Course Description
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. Prerequisites: 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
* 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
