Intro to Unsupervised Machine Learning

Overview

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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.

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