Machine Learning Foundations : Supervised Learning
Yehoshua, Roi
Sold by GreatBookPrices, Columbia, MD, U.S.A.
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New - Soft cover
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Add to basketSold by GreatBookPrices, Columbia, MD, U.S.A.
AbeBooks Seller since 6 April 2009
Condition: New
Quantity: Over 20 available
Add to basketThe Essential Guide to Machine Learning in the Age of AI
Machine learning stands at the heart of today's most transformative technologies: advancing scientific discovery, reshaping industries, and transforming everyday life. From large language models to medical diagnosis and autonomous vehicles, the demand for robust, principled machine learning models has never been greater.
Machine Learning Foundations, Volume 1: Supervised Learning, offers a comprehensive and accessible roadmap to the core algorithms and concepts behind modern AI systems. Balancing mathematical rigor with hands-on implementation, this book not only teaches how machine learning works, but why it works. As part of a three-volume series, Volume 1 lays the foundation for mastering the full landscape of modern machine learning, including deep learning, large language models, and cutting-edge research.
Each chapter introduces core ideas with clear intuition, supports them with rigorous mathematical derivations where appropriate, and demonstrates how to implement the methods in Python, while also addressing practical considerations such as data preparation and hyperparameter tuning. Exercises at the end of each chapter, both theoretical and programming-based, reinforce understanding and promote active learning.
The book includes hundreds of fully annotated code examples, available on GitHub at github.com/roiyeho/ml-book, along with six comprehensive online appendices covering essential background in linear algebra, calculus, probability, statistics, optimization, and Python libraries such as NumPy, Pandas, and Matplotlib.
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Roi Yehoshua is a professor in the Department of Electrical and Computer Engineering at Northeastern University, where he develops and teaches graduate courses in machine learning and data science. With over two decades of experience spanning academia and industry, he has developed and taught a wide range of machine learning courses, including pioneering the university's first course on Large Language Models. His writing on machine learning has reached over 200,000 readers worldwide through platforms like Medium and Towards Data Science.
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