This book introduces mathematical techniques, key algorithms, and Python implementations for building machine learning models for unannotated data. It bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. Vaibhav Verdhan introduces hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic business decisions.

<ASIN: 1617298727>

Author: Vaibhav Verdhan
Publisher: Manning
Date: July 2025
Pages: 352
ISBN: 978-1617298721
Print: 1617298727
Kindle: B0FCYVBN1W
Audience: Python developers interested in machine learning
Level: Intermediate/Advanced
Category: Artificial Intelligence

Topics include:

• 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

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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