Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.
The recent explosion of interest in data science, data mining, and related disciplines has been mirrored by an explosion in book titles on these same topics. One of the best ways to decide which books could be useful for your career is to look at which books others are reading. This post details the 10 most popular titles in Amazon's Artificial Intelligence & Machine Learning Books category as of Nov 24, 2016, skipping over repeated titles as well as titles which have been obviously miscategorized and are of no use to our readers.
Note: KDnuggets gets absolutely no royalties from Amazon - this list is presented only to help our readers evaluate interesting books.
1. Deep Learning (Adaptive Computation and Machine Learning series)
Ian Goodfellow, Yoshua Bengio, Aaron Courville
4.8 out of 5 stars (4 reviews)
This sums it up nicely:
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
2. Amazon Echo: The Ultimate Guide to Learn Amazon Echo In No Time
4.2 out of 5 stars (74 reviews)
Paperback, $9.95 (Kindle, $2.88)
Learn all about building custom and smart home skills to make your Echo even more personal! Smooth, secure, fast, and foolproof, Alexa Skills Kit helps you keep Echo learning. This guide is also suited for the intermediate, tech-savvy users who want a quick, sure-fire way of getting to know their Echo device, and how best to acquaint themselves with the Echo’s functionality and customizable potential.
3. Gödel, Escher, Bach: An Eternal Golden Braid
Douglas R. Hofstadter
4.5 out of 5 stars (472 reviews)
If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.
4. Make Your Own Neural Network
4.2 out of 5 stars (65 reviews)
Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work.
This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included.
5. Python Machine Learning
4.3 out of 5 stars (80 reviews)
- Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization
- Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms
- Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets
6. Superintelligence: Paths, Dangers, Strategies
3.9 out of 5 stars (284 reviews)
Paperback, $13.72 (Kindle, $8.13)
Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence.
This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy.
7. Markov Models: Master Data Science and Unsupervised Machine Learning in Python
4.0 out of 5 stars (1 reviews)
We’re going to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick. We’re going to talk about how Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO. We’ll build language models that can be used to identify a writer and even generate text - imagine a machine doing your writing for you.
8. Machine Learning: The New AI: The MIT Press Essential Knowledge Series
3.5 out of 5 stars (2 reviews)
Audio, $14.95 (Paperback, $10.63)
Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
9. An Introduction to Statistical Learning: with Applications in R
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
4.8 out of 5 stars (127 reviews)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
10. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
4.7 out of 5 stars (15 reviews)
This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning.