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Models and Algorithms for Unlabeled Data

Published by Manning
Distributed by Simon & Schuster
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Discover all-practical implementations of the key algorithms and models for handling unlabelled data. Full of case studies demonstrating how to apply each technique to real-world problems.

Models and Algorithms for Unlabeled Data introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data.

You’ll master everything from kmeans and hierarchical clustering, to advanced neural networks like GANs and Restricted Boltzmann Machines. You’ll learn the business use case for different models, and master best practices for structured, text, and image data. 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. 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.