Unsupervised machine learning is a category of artificial intelligence where algorithms learn from unlabeled data. Unlike supervised learning, there are no predefined outputs or training labels provided to guide the model.
These algorithms uncover hidden patterns, intrinsic structures, and relationships within data. This makes them highly valuable in exploratory data analysis and tasks involving high-dimensional or unstructured datasets.
Common techniques include clustering methods like k-means and DBSCAN, dimensionality reduction using PCA or t-SNE, and anomaly detection in cybersecurity and finance.
Best Unsupervised Machine Learning Books
Unsupervised learning is foundational for building recommendation systems, customer segmentation models, and feature extraction pipelines.
This article presents the best unsupervised machine learning books to help professionals master algorithmic logic, data pattern discovery, and scalable learning systems without labels.
The Hundred-Page Machine Learning Book (The Hundred-Page Books)
- Burkov, Andriy (Author)
- English (Publication Language)
- 160 Pages - 01/13/2019 (Publication Date) - Andriy Burkov (Publisher)
Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
- Patel, Ankur A. (Author)
- English (Publication Language)
- 362 Pages - 04/02/2019 (Publication Date) - O'Reilly Media (Publisher)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
- Géron, Aurélien (Author)
- English (Publication Language)
- 861 Pages - 11/08/2022 (Publication Date) - O'Reilly Media (Publisher)
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
- Huyen, Chip (Author)
- English (Publication Language)
- 386 Pages - 06/21/2022 (Publication Date) - O'Reilly Media (Publisher)
Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences)
- Waggoner, Philip D. (Author)
- English (Publication Language)
- 70 Pages - 01/28/2021 (Publication Date) - Cambridge University Press (Publisher)
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
- Amita Kapoor (Author)
- English (Publication Language)
- 698 Pages - 10/06/2022 (Publication Date) - Packt Publishing (Publisher)
Machine Learning Foundations and Applications: A Practical Guide to Supervised, Unsupervised, and Reinforcement Learning
- E., Jarrel (Author)
- English (Publication Language)
- 230 Pages - 05/09/2025 (Publication Date) - Independently published (Publisher)
Unsupervised Learning Algorithms
- Hardcover Book
- English (Publication Language)
- 568 Pages - 05/09/2016 (Publication Date) - Springer (Publisher)
Grokking Machine Learning
- Serrano, Luis (Author)
- English (Publication Language)
- 512 Pages - 12/14/2021 (Publication Date) - Manning (Publisher)
Principles of Machine Learning: The Three Perspectives
- Hardcover Book
- Wang, Wenmin (Author)
- English (Publication Language)
- 562 Pages - 10/27/2024 (Publication Date) - Springer (Publisher)
Machine Learning Crash Course for Engineers
- Hardcover Book
- Hossain, Eklas (Author)
- English (Publication Language)
- 473 Pages - 01/03/2024 (Publication Date) - Springer (Publisher)
Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience)
- English (Publication Language)
- 398 Pages - 06/11/1999 (Publication Date) - MIT Press (Publisher)
Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python
- Tatsat, Hariom (Author)
- English (Publication Language)
- 429 Pages - 12/15/2020 (Publication Date) - O'Reilly Media (Publisher)
Practical Machine Learning in R
- Nwanganga, Fred (Author)
- English (Publication Language)
- 464 Pages - 05/27/2020 (Publication Date) - Wiley (Publisher)
Feature and Dimensionality Reduction for Clustering with Deep Learning (Unsupervised and Semi-Supervised Learning)
- Hardcover Book
- Ros, Frederic (Author)
- English (Publication Language)
- 279 Pages - 01/03/2024 (Publication Date) - Springer (Publisher)
Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications (Advanced Data Analytics Book 1)
- Amazon Kindle Edition
- Chapmann, Joshua (Author)
- English (Publication Language)
- 103 Pages - 06/21/2017 (Publication Date)
Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI
- Monarch, Robert (Munro) (Author)
- English (Publication Language)
- 424 Pages - 07/20/2021 (Publication Date) - Manning (Publisher)
Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems
- Géron, Aurélien (Author)
- English (Publication Language)
- 800 Pages - 12/02/2025 (Publication Date) - O'Reilly Media (Publisher)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Language Published: English
- Binding: Hardcover
- Comes in Good condition
- Hardcover Book
- Hastie, Trevor (Author)
- English (Publication Language)
- 767 Pages - 02/09/2009 (Publication Date) - Springer (Publisher)
First Little Readers Parent Pack: Guided Reading Level A: 25 Irresistible Books That Are Just the Right Level for Beginning Readers
- Schecter, Deborah (Author)
- English (Publication Language)
- 25 Pages - 10/01/2010 (Publication Date) - Scholastic Teaching Resources (Teaching Strategies) (Publisher)
Gaining proficiency in unsupervised learning allows you to extract meaningful insights from raw and unannotated data sources.
The best unsupervised machine learning books provide essential knowledge on hierarchical clustering, self-organizing maps, latent variable models, and generative techniques like autoencoders.
These resources also explore challenges unique to unsupervised workflows, such as determining the optimal number of clusters, dealing with sparse datasets, and evaluating model effectiveness without ground truth.
Professionals in fields such as natural language processing, image recognition, and customer analytics benefit from a deep understanding of unsupervised algorithms and data representation strategies.
With the right foundation, you can design intelligent systems that autonomously adapt to data complexity and uncover patterns that drive innovation.
