The Best Unsupervised Learning eBooks of All Time

Discover the most influential unsupervised learning ebooks, recommended by leaders, experts, and readers worldwide

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Recommendations by Sfisu Andrei, John Ahlquist, Bettina Grun, Nicolas Lomenie and 7 others

Not sure what to read? Our AI can suggest the most recommended Unsupervised Learning books!

1
Book Cover of Rowel Atienza - Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition

By Rowel Atienza – Associate Professor at the Electrical and Electronics Engineering Institute of the University of the Philippines (you?) 

4.60
| 2020 | 512 Pages
Recommended for: 
Experienced practitioners of artificial intelligence. Intermediate to Advanced readers.
You will:
  • Apply mutual information maximization for unsupervised learning.
  • Identify pixel-wise class in image segmentation.
  • Learn object detection for bounding box and class identification.
  • Understand advanced neural network architectures like ResNet and DenseNet.
  • Implement autoregressive models such as autoencoders, VAEs, and GANs.
Reviews:
Practical Projects
Clear Explanations
Gradual Complexity
Hands-On Approach
Updated Content
GPU Requirement
Limited NLP Coverage
Bernardo NunesGreat visuals, code, and math. The book delivers what the deep learning practitioner needs: advanced content with replicable and reproducible results. I highly recommend this great book by Rowel Atienza
Tristan BehrensAdvanced Deep Learning with TensorFlow 2 and Keras - Second Edition is a good and big step into an advanced practice direction. It's a brilliant book and consider this as a must-read for all
Danny MaI highly recommend this book for the curious data practitioner who wants to further solidify their knowledge of deep learning. The companion GitHub code repository is very useful and provides a hassle-free way to actually experiment with the various ideas presented in the book. If you enjoy reading technical books, but also enjoy experimenting with real code, and didn't think the two could be combined effectively - this book is here to change your perspective!
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2
Book Cover of Ankur A. Patel - Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

By Ankur A. Patel – Vice President of Data Science at 7Park Data, Machine Learning Expert (you?) 

4.55
| 2019 | 359 Pages
Recommended for: 
Data scientists and machine learning enthusiasts with programming and some machine learning experience. Intermediate to Advanced readers.
You will:
  • Apply unsupervised learning to large datasets
  • Uncover hidden patterns in data
  • Detect anomalies in data
  • Perform automatic feature engineering
  • Generate synthetic datasets
Reviews:
Practical Examples
Real-world Applications
Hands-on Approach
Clear Code
Visualizations
Lack of Graphics
Poor Explanations
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Unsupervised Learning Book made by AI

By TailoredRead – AI that creates personalized books for you 

4.98
| 2025 | 30-300 pages
Learn Unsupervised Learning faster with a book created specifically for you by state-of-the-art AI. Our AI has vast knowledge of Unsupervised Learning, and will craft a custom-tailored book for you in just 10 minutes. This tailored book addresses YOUR unique interests, goals, knowledge level, and background. Available for online reading, PDF download, and Kindle, your custom book will provide personalized insights to help you learn faster, expand your horizons, and accomplish your goals. Embark on your Unsupervised Learning learning journey with a personalized book - made exclusively for you.
Recommended for: 
All readers across all knowledge levels.
You will:
  • Get an Unsupervised Learning book tailored to your interests, goals, and background
  • Receive a book precisely matching your background and level of knowledge
  • Select which topics you want to learn, exclude the topics you don't
  • Define your learning goals and let your book guide you to accomplish them
  • Get all the knowledge you need consolidated into a single focused book
Reviews:
Insightful
Focused
Highly Personalized
Easy to Read
Engaging
Actionable
Up-to-Date
3
Book Cover of Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery - Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)

By Charles Bouveyron – Full Professor of Statistics at Université Côte d'Azur (you?) and 3 more 

4.50
| 2019 | 446 Pages
Recommended for: 
Advanced undergraduates in data science, researchers, and practitioners. Intermediate to Advanced readers.
Recommended by John Ahlquist, Bettina Grun, Zdenek Hlavka and 3 others
John AhlquistBouveyron, Celeux, Murphy, and Raftery pioneered the theory, computation, and application of modern model-based clustering and discriminant analysis. Here they have produced an exhaustive yet accessible text, covering both the field's state of the art as well as its intellectual development. The authors develop a unified vision of cluster analysis, rooted in the theory and computation of mixture models. Embedded R code points the way for applied readers, while graphical displays develop intuition about both model construction and the critical but often-neglected estimation process. Building on a series of running examples, the authors gradually and methodically extend their core insights into a variety of exciting data structures, including networks and functional data. This text will serve as a backbone for graduate study as well as an important reference for applied data scientists interested in working with cutting-edge tools in semi- and unsupervised machine learning
Bettina GrunThis book, written by authoritative experts in the field, gives a comprehensive and thorough introduction to model-based clustering and classification. The authors not only explain the statistical theory and methods, but also provide hands-on applications illustrating their use with the open-source statistical software R. The book also covers recent advances made for specific data structures (e.g. network data) or modeling strategies (e.g. variable selection techniques), making it a fantastic resource as an overview of the state of the field today
Zdenek HlavkaIn my opinion, the overall quality of this impactful and intriguing book can be expressed by concluding that it is a perfect fit to the Cambridge Series in Statistical and Probabilistic Mathematics, characterized as a series of high-quality upper-division textbooks and expository monographs containing applications and discussions of new techniques while emphasizing rigorous treatment of theoretical methods
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