Unsupervised Learning

1. Introduction to Unsupervised Learning and K Means

2. Selecting a clustering algorithm

3. Dimensionality Reduction

This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.

To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

By the end of this course you should be able to:- Explain the kinds of problems suitable for Unsupervised Learning approaches. 

- Explain the curse of dimensionality, and how it makes clustering difficult with many features. 

- Describe and use common clustering and dimensionality-reduction algorithms. 

- Try clustering points where appropriate, compare the performance of per-cluster models. 

- Understand metrics relevant for characterizing clusters.