Navigate through the intricacies of Unsupervised Learning and Clustering in this hands-on course. Skip the high-level libraries and build core aspects of unsupervised learning methods from scratch, including k-Means, mini-batch k-Means, Principal Component Analysis, and DBSCAN. Learn to assess cluster quality with crucial clustering metrics like homogeneity, completeness, and v-metric.
Overview
Syllabus
- Lesson 1: Understanding Clustering with k-Means Algorithm Basics
- Lesson 2: Enhancing Machine Learning Expertise: Mini-Batch K-Means Clustering Explained
- Lesson 3: A Practical Introduction to Principal Component Analysis (PCA)
- Lesson 4: Mastering DBSCAN: From Basics to Implementation
