Data Without Labels: Practical unsupervised machine learning

by Vaibhav Verdhan

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems.

In Data Without Labels you’ll

• Fundamental building blocks and concepts of machine learning and unsupervised learning
• Data cleaning for structured and unstructured data like text and images
• Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering
• Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE
• Association rule algorithms like aPriori, ECLAT, SPADE
• Unsupervised time series clustering, Gaussian Mixture models, and statistical methods
• Building neural networks such as GANs and autoencoders
• Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling
• Association rule algorithms like aPriori, ECLAT, and SPADE
• Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask
• How to interpret the results of unsupervised learning
• Choosing the right algorithm for your problem
• Deploying unsupervised learning to production
• Maintenance and refresh of an ML solution

Data Without Labels 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. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge.

Foreword by Ravi Gopalakrishnan.

About the technology

Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels—or unsupervised learning—turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how.

About the book

Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You’ll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you’ll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end.

What's inside

• Master unsupervised learning algorithms
• Real-world business applications
• Curate AI training datasets
• Explore autoencoders and GANs applications

About the reader

Intended for data science professionals.

639 pages, Kindle Edition

Published June 24, 2025

Reviews

[krishna Chaitanya · Rating 5 out of 5]

In the last few months, we have seen AI take strides. While we are still digesting GPTs, AI agents have already arrived. While the advances in AI are happening at a rapid pace, anyone who wants to get into AI should still get the basics right. Data without Labels is a great book that patiently teaches you ML and more specifically the unsupervised learning, deep learning and also the basics of Gen AI. The book itself is organized into three sections with increasing levels of complexity of the topics. There are ample code examples throughout the book that will help you get your hands dirty. There is also an appendix with an introduction to the most important mathematical concepts that are used across the book. The author has patiently included relevant real-world examples of the core concepts throughout the book. Highly recommended to jump start with unsupervised learning, which is perhaps the most important topics to master in today's world.

[Avinash · Rating 5 out of 5]

I just finished Data Without Labels by Vaibhav Verdhan and found it refreshingly practical. It’s packed with Python code (scikit‑learn, Pandas, TensorFlow, Flask) that’s ready to run out of the box.The author balances math and implementation beautifully—whether it’s k‑Means, DBSCAN, PCA, autoencoders, or GANs.I especially liked the real-world case studies—from retail to banking—which show how to spot customer segments, anomalies, or patterns in unstructured data . The deployment sections are a bonus, guiding you through turning experiments into production pipelines. If you’re ready to go beyond supervised models and actually use unlabeled data in your projects, this book is a great next step. Highly recommended!

[Leonardo Silva · Rating 5 out of 5]

Great hands-on guide to unsupervised learning!
Really enjoyed this book—it breaks down complex topics like clustering, PCA, and GANs in a way that’s easy to follow, especially with the Python examples. It’s super practical, not just theory, and even covers deployment and real-world use cases. Perfect if you’re working with unlabeled data and want to level up your ML skills.

[Simone Sguazza · Rating 5 out of 5]

This book covers all the fundamentals for the unsupervised learning field, a topic which is always not well covered in the data science path like the supervised learning field.
Here you can find the subject applied to many data type like tabular data, text and timeseries.
I recommend this book to all the people who want to master the unsupervised learning subject.