Beginner Machine Learning Lecture 9 - Dimensionality Reduction

Welcome to Lecture 9 of our Beginner Machine Learning course! In this session, we explore the essential concept of dimensionality reduction and its significance in machine learning.

What You’ll Learn:

What is Dimensionality Reduction?: Gain a clear understanding of dimensionality reduction techniques and why they are crucial for simplifying complex datasets.
Key Techniques: Discover popular methods such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) that help reduce the number of features while preserving important information.
Applications: Learn how dimensionality reduction is applied in various fields, including data visualization and improving model performance.