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A practical guide to solving tricky business problems using Python-based unsupervised learning techniques.A great introduction to the subject of unsupervised learning techniques.
Unsupervised Learning with Python contains comprehensive coverage of the mathematical foundations, algorithms, and practical implementations of unsupervised learning. In this focused guide, you’ll learn to use this hands-off machine learning approach to uncover patterns and trends in your data, and support sound strategic decisions for your business.
In Unsupervised Learning with Python, you’ll learn:
- Fundamental building blocks and concepts of unsupervised learning
- How to interpret the results of unsupervised learning
- Deploying unsupervised learning to production
- Best practices for debugging and improving your models
- Data cleaning for structured and unstructured data
- Choosing the right algorithm for your problem
- Business use cases for unsupervised learning
About the Technology
Unsupervised learning algorithms draw inferences from unannotated data sets. The self-organizing approach to machine learning is great for spotting patterns a human might miss.About the book
Unsupervised Learning with Python teaches you to apply the full spectrum of unsupervised learning algorithms. Each new algorithm is introduced with a case study for retail, aviation, banking, and more—and you’ll develop a Python solution to fix each of these real-world problems.The book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You’ll learn the business use case for different unsupervised learning models, and master best practices for structured, text, and image data. At the end of each chapter, you’ll find quizzes, practice datasets, and links to research papers to help you lock in what you’ve learned and expand your knowledge.
