Data Without Labels: Practical unsupervised machine learning
Language: English
Published by Manning, 2025
- Softcover
- New
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
AbeBooks seller since March 25, 2015
Condition: New
£ 61.23
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Add to basketItem description from seller
In English.
Seller Inventory # ria9781617298721_new
- Title
- Data Without Labels: Practical unsupervised machine learning
- Author
- Verdhan, Vaibhav
- Publisher
- Manning
- Publication year
- 2025
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1617298727
- ISBN 13
- 9781617298721
- Item weight
- 768 grams
In Models and Algorithms for Unsupervised Learning you'll learn:
- Fundamental building blocks and concepts of machine learning and unsupervised learning
- Data cleaning for structured and unstructured data like text and images
- Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
- Building neural networks such as GANs and autoencoders
- How to interpret the results of unsupervised learning
- Choosing the right algorithm for your problem
- Deploying unsupervised learning to production
- Business use cases for machine learning and unsupervised learning
Models and Algorithms for Unsupervised Learning introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You'll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don't get bogged down in theory―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.
about the technology
Unsupervised learning and machine 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
Models and Algorithms for Unsupervised Learning teaches you to apply a full spectrum of machine learning algorithms to raw 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."Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Ria Christie Collections
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