Dimitris Poulopoulos

Machine Learning Engineer | Researcher

Resources

A place for top rated books in Data Science

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Books


Author: Andriy Burkov
Latest Edition: First
Publisher: Andriy Burkov
Format: eBook / Hardcover / Paperback

The Hundred-Page Machine Learning Book: The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field.


Author: Yaser Abu Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin
Latest Edition: First
Publisher: AMLBook
Format: Kindle / Hardcover

Learning From Data: This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know.


Author: Christopher M. Bishop
Latest Edition: Second
Publisher: Springer
Format: Kindle / Hardcover / Paperback

Pattern Recognition and Machine Learning: This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.


Author: Aurélien Géron
Latest Edition: Second
Publisher: O’Reilly Media
Format: Kindle / Paperback

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.


Author: Kevin P. Murphy
Latest Edition: First (second edition coming soon)
Publisher: The MIT Press
Format: eBook / Hardcover

Machine Learning: A Probabilistic Perspective: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.


Author: Jeremy Howard, Sylvain Gugger
Latest Edition: First
Publisher: O’Reilly Media
Format: Kindle / Paperback

Deep Learning for Coders with Fastai and PyTorch: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.



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