Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous books.
This book blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible programming languages to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
Pages : | /Paperback N/A |
Size : | HTML |
Downloads: | 54 |
Created: | 2020-08-29 |
License: | Creative Commons Attribution-ShareAlike 3.0 Unported License |
Author(s): | Wikipedia Contributors |
Warning: Trying to access array offset on false in /home/tutovnfz/public_html/amp/article-amp.php on line 263
Others related eBooks about Machine Learning: The Complete Guide
Everything you really need to know in Machine Learning in a hundred pages!
Download free course Python Machine Learning Projects, pdf file on 135 pages by Lisa Tagliaferri, Michelle Morales, Ellie Birkbeck, Alvin Wan.
This book is about making machine learning models and their decisions interpretable. After..., download free Machine Learning tutorial in PDF (312 pages) created by Christoph Molnar .
This book explains to you how to make (supervised) machine learning models interpretable.
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.