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Book Overview & Buying
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Table Of Contents
Cluster Analysis and Unsupervised Machine Learning in Python
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Cluster Analysis and Unsupervised Machine Learning in Python
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Overview of this book
Unlock the power of unsupervised machine learning with this comprehensive course on cluster analysis using Python. Begin your journey with a solid introduction to unsupervised learning, understanding its significance and practical applications. You'll start with the basics of K-Means clustering, progressing through detailed theoretical explanations and hands-on coding exercises designed to deepen your understanding.
As you advance, the course delves into hierarchical clustering, providing a thorough walkthrough of agglomerative clustering techniques. You'll learn to interpret dendrograms and apply these methods to intriguing case studies like evolutionary analysis and political tweet analysis. This section ensures you gain practical skills in applying hierarchical clustering to diverse datasets.
The course culminates with an exploration of Gaussian Mixture Models (GMMs), where you'll compare GMM with K-Means and understand the advantages of each. You'll also learn about the Expectation-Maximization algorithm and practical issues related to GMMs, enhancing your ability to handle complex clustering tasks. With additional modules on setting up your Python environment and effective learning strategies, this course equips you with the tools and knowledge to excel in unsupervised machine learning.
Table of Contents (9 chapters)
Welcome
Getting Set Up
Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Gaussian Mixture Models (GMMs)
Setting Up Your Environment (Appendix)
Extra Help With Python Coding for Beginners (Appendix)
Effective Learning Strategies for Machine Learning (Appendix)
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