Mastering Unsupervised Learning

At a Glance

"Mastering Unsupervised Learning" is a practical course designed to provide participants with a comprehensive understanding of unsupervised learning techniques. Participants will gain hands-on experience in applying clustering, dimensionality reduction, and anomaly detection algorithms to real-world datasets using Python. 

Objectives

By the end of the course, participants will be equipped to:

• Understand the principles and applications of unsupervised learning in real-world scenarios
• Implement clustering algorithms such as k-means, hierarchical clustering, etc
• Apply dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE)
• Detect anomalies and outliers in datasets using unsupervised learning techniques
• Evaluate and interpret the results of unsupervised learning algorithms 

Prerequisite

Participants should have a basic understanding of Python programming and familiarity with fundamental concepts in machine learning. Prior exposure to supervised learning and basic statistics is recommended but not required.