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.
Data Without Labels: Practical unsupervised machine learning
- Verdhan, Vaibhav (Author)
- English (Publication Language)
- 352 Pages - 07/08/2025 (Publication Date) - Manning (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)
Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
- Patel, Ankur A. (Author)
- English (Publication Language)
- 359 Pages - 04/16/2019 (Publication Date) - O'Reilly Media (Publisher)
Machine Learning Essentials You Always Wanted to Know: A Hands-On Beginner’s Guide to Mastering AI, Supervised, Unsupervised, and Deep Learning Algorithms
- Parikh, Dhairya (Author)
- English (Publication Language)
- 274 Pages - 07/04/2025 (Publication Date) - Vibrant Publishers (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)
The Hundred-Page Machine Learning Book (The Hundred-Page Books)
- Amazon Kindle Edition
- Burkov, Andriy (Author)
- English (Publication Language)
- 160 Pages - 01/07/2022 (Publication Date) - Andriy Burkov (Publisher)
Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning (Multivariate Analysis Book 1)
- Amazon Kindle Edition
- Kassambara, Alboukadel (Author)
- English (Publication Language)
- 189 Pages - 11/17/2017 (Publication Date)
The Unsupervised Learning Workshop: Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
- Jones, Aaron (Author)
- English (Publication Language)
- 550 Pages - 07/29/2020 (Publication Date) - Packt Publishing (Publisher)
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
- Kapoor, Amita (Author)
- English (Publication Language)
- 698 Pages - 10/06/2022 (Publication Date) - Packt Publishing (Publisher)
Machine Learning Algorithms in Depth
- Smolyakov, Vadim (Author)
- English (Publication Language)
- 328 Pages - 08/27/2024 (Publication Date) - Manning (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)
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)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
- 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)
Machine Learning. Supervised and unsupervised learning, latent semantic indexing, spectral clustering and Bellman equations
- Yadav, Ashok Kumar (Author)
- English (Publication Language)
- 196 Pages - 12/28/2022 (Publication Date) - Grin Verlag (Publisher)
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
- Kolodiazhnyi, Kirill (Author)
- English (Publication Language)
- 512 Pages - 01/24/2025 (Publication Date) - Packt Publishing (Publisher)
Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R
- Dangeti, Pratap (Author)
- English (Publication Language)
- 442 Pages - 07/21/2017 (Publication Date) - Packt Publishing (Publisher)
Unsupervised Learning with R
- Pacheco, Erik Rodriguez (Author)
- English (Publication Language)
- 192 Pages - 12/03/2015 (Publication Date) - Packt Publishing (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)
Applied Unsupervised Learning with R: Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
- Amazon Kindle Edition
- Malik, Alok (Author)
- English (Publication Language)
- 322 Pages - 03/27/2019 (Publication Date) - Packt Publishing (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.
