- Introduction to Unsupervised ML

This session will introduce the principles and applications of unsupervised machine learning (UML). Students will learn about the different types of UML problems, and they will explore some of the most popular UML algorithms, such as PCA, SVD, and NMF.

Notebook(s)

• Hands-on Intro to Dimensionality reduction and Clustering

• Assignment for the UML

• Assignment for the UML Solution

Recommended Datacamp exercises:

• Python

Recommended Readings and resources

• Python Data Science Handbook Chapter 5

  • What Is Machine Learning?
  • Introducing Scikit-Learn
  • Feature Engineering
  • In Depth: Principal Component Analysis
  • In Depth: k-Means Clustering

• Implementation tutorials on YT PCA and K-means from this list

Intro slides

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