Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientists and machine learning practitioners will learn hands-on how to apply unsupervised learning to large unlabeled datasets using Python tools. You’ll uncover hidden patterns, gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets.
Author Ankur Patel—an applied machine-learning researcher and data scientist with expertise in financial markets—provides the concepts, intuition, and tools necessary for you to apply this technology to problems you tackle every day. Through the course of this book, you’ll learn how to build production-ready systems with Python.
Chapters for this Early Release edition will be available as they are completed, including the first five chapters in this initial release:
- Examine the difference between supervised and unsupervised learning, and the relative strengths and weaknesses of each
- Set up and manage a machine learning project end-to-end—everything from data acquisition to building a model and implementing a solution in production
- Explore dimensionality reduction algorithms that learn the underlying structure of a dataset’s most salient information
- Build a credit card fraud detection system using dimensionality reduction methods