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Hey, I want to learn a lot about machine learning and AI, I'd rather you suggest generic books about the concept rather than language specific.
If you're interested in statistical analysis or image analysis then this book is for you, it covers all aspects of machine learning and a great start for Bayesian learning.
If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.
Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.
Although this book is perfect for those with prior knowledge I can't recommend it for newbies. If you know a bit then great buy it or if you like being thrown in the deep end then pick it up.
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.
Simple, effective and very dense with information. This book is honestly the best I've read as a beginner and then moving into a career. A must have even for just a hobbyist.
This book is brilliant for anyone who wants to learn a bit more about machine learning. It has great example code written in python and also their real world examples are presented in a simplistic manner making your learning all that bit quicker.
Easy to understand and a very good read.