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Unsupervised Learning with Python

Vaibhav Verdhan
  • MEAP began November 2020
  • Publication in Spring 2022 (estimated)
  • ISBN 9781617298721
  • 250 pages (estimated)
  • printed in black & white

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A great introduction to the subject of unsupervised learning techniques.

Richard Vaughan
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A practical guide to solving tricky business problems with Python-based unsupervised learning techniques.

In Unsupervised Learning with Python, you’ll learn:

  • Fundamental building blocks and concepts of unsupervised learning
  • Building neural networks such as GANs and autoencoders
  • Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
  • How to interpret the results of unsupervised learning
  • Deploying unsupervised learning to production
  • Data cleaning for structured and unstructured data
  • Choosing the right algorithm for your problem
  • Business use cases for unsupervised learning


Unsupervised Learning with Python teaches you to solve common business problems with the power of unsupervised learning. It’s filled with comprehensive coverage of unsupervised learning’s mathematical foundations, key algorithms, and Python libraries including scikit-learn, NLTK, gensim, TensorFlow, Keras, PyTorch, and more. 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.

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. You’ll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. 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.

about the reader

For developers and data scientists interested in applying UL to their projects. Basic Python experience required.

about the author

Vaibhav Verdhan is a seasoned data science professional with rich experience across geographies and domains. He has led multiple engagements in machine learning and artificial intelligence. A leading industry expert, Vaibhav is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland where he works as a principal data scientist.

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Excellent deep dive into unsupervised learning with Python!

Todd Cook
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