Wow - it is March already? I can't believe it's been a whole month since our last Free Data Science books blog post.
How time flies.
This month we have 3 books that I think will define what it means to be a Data Scientist over the coming decade. I think it'll be less about data analysis, algorithms and stab-in-the-dark attempts at optimising your models, and more about image and video analysis, analysis of natural language and intelligent optimisation of processes. The 3 books this month really hit the spot on all counts.
I hope you'll take the time to check them out - if you're serious about becoming a Data Scientist, these books will help you plan out your future.
Enjoy!
More...
This month we highlight 3 books:
They're all FREE, so help yourselves...
Disclosure: The FREE ebooks are free to download but other links in this post may contain affiliate links. This means that if you click one of the links and make a purchase we may receive a small commission at no extra cost to you.
You can find further details in our TCs
By Richard Szeliski
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
More than just a source of “recipes”, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques.
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By Steven Bird, Ewan Klein, and Edward Loper
This is a book about Natural Language Processing. By "natural language" we mean a language that is used for everyday communication by humans; languages like English, Hindi or Portuguese. In contrast to artificial languages such as programming languages and mathematical notations, natural languages have evolved as they pass from generation to generation, and are hard to pin down with explicit rules. We will take Natural Language Processing — or NLP for short — in a wide sense to cover any kind of computer manipulation of natural language. At one extreme, it could be as simple as counting word frequencies to compare different writing styles. At the other extreme, NLP involves "understanding" complete human utterances, at least to the extent of being able to give useful responses to them.
Technologies based on NLP are becoming increasingly widespread. For example, phones and handheld computers support predictive text and handwriting recognition; web search engines give access to information locked up in unstructured text; machine translation allows us to retrieve texts written in Chinese and read them in Spanish; text analysis enables us to detect sentiment in tweets and blogs. By providing more natural human-machine interfaces, and more sophisticated access to stored information, language processing has come to play a central role in the multilingual information society.
This book provides a highly accessible introduction to the field of NLP. It can be used for individual study or as the textbook for a course on natural language processing or computational linguistics, or as a supplement to courses in artificial intelligence, text mining, or corpus linguistics. The book is intensely practical, containing hundreds of fully-worked examples and graded exercises.
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By Roberto Battiti and Mauro Brunato
This newly revised book presents two topics which are in most cases separated: machine learning (the design of flexible models from data) and intelligent optimization (the automated creation and selection of improving solutions). More and more innovative people (lionhearted?) can now master the source of power arising from LION techniques to solve problems, improve businesses, create new prescriptive analytics applications.
Powerful tools are not only for cognoscenti and this book does a serious effort to distinguish the paradigm shift brought about by machine learning and intelligent optimization methods from the fine details, and it does not refrain from presenting concrete examples and vivid sticky images (made to stick to your mind).
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