Unsupervised Learning (Coursera)

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

Who should take this course?

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

What skills should you have?

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

Completing this course will count towards your learning in any of the following programs:

- IBM Machine Learning Professional Certificate

- IBM Introduction to Machine Learning Specialization

Syllabus

WEEK 1

Introduction to Unsupervised Learning and K Means

This module introduces Unsupervised Learning and its applications. One of the most common uses of Unsupervised Learning is clustering observations using k-means. In this module you become familiar with the theory behind this algorithm and put it in practice in a demonstration.

WEEK 2

Selecting a clustering algorithm

In this module you become familiar with some of the computational hurdles around clustering algorithms, and how different clustering implementations try to overcome them. After a brief recapitulation of common clustering algorithms, you will learn how to compare them and select the clustering technique that best suits your data.

WEEK 3

Dimensionality Reduction

This module introduces dimensionality reduction and Principal Component Analysis, which are powerful techniques for big data, imaging, and pre-processing data. At the end of this module, you will have all the tools in your toolkit to highlight your Unsupervised Learning abilities in your final project.