Welcome to this comprehensive tutorial on unsupervised learning techniques in machine learning! In this video, we’ll break down three key areas:
Clustering Fundamentals : Learn about K-means, hierarchical clustering, and how to evaluate clustering quality.
Anomaly & Novelty Detection : Discover methods like density-based and isolation-based detection, as well as one-class learning.
Dimensionality Reduction Techniques : Understand PCA, t-SNE, LLE, and autoencoders to simplify high-dimensional data.
Whether you’re a software engineer new to machine learning or looking to deepen your understanding of unsupervised learning, this video is for you. We’ll explain complex concepts with real-world analogies and clear examples—no code, just theory!
đź“Ś Key Topics Covered :
Goals and applications of clustering
Detecting rare events with anomaly detection
Overcoming the curse of dimensionality