This advanced course explores unsupervised machine learning, emphasizing dimensionality reduction and clustering methods. Using the Iris dataset, you will apply different methods and interpret the practical implications of the clusters identified.
Overview
Syllabus
- Lesson 1: Exploring and Visualizing the Iris Dataset
- Lesson 2: Unraveling the Knots of K-means Clustering
- Lesson 3: Unsupervised Learning: Hands-on with K-means Clustering
- Lesson 4: Exploring and Implementing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm
- Lesson 5: Introduction to Principal Component Analysis and Dimensionality Reduction
- Lesson 6: Unveiling Independent Component Analysis: Theory, Implementation, and Insights
- Lesson 7: Unveiling High-Dimensional Data: An Introduction to t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Lesson 8: Understanding and Comparing Clustering and Dimension Reduction Techniques
