What better way to enjoy this spring weather than with some free machine learning and data science ebooks? Right? Right?
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Feb 16, 2018 Machine learning is an application of artificial intelligence that gives a system an ability to automatically learn and improve from experiences without being explicitly programmed. In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular). Mining of Massive Datasets. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming.
Here is a quick collection of such books to start your fair weather study off on the right foot. The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together. A mix of classic and contemporary titles, hopefully you find something new (to you) and of interest here.
1. Think Stats: Probability and Statistics for Programmers
By Allen B. Downey
Think Stats is an introduction to Probability and Statistics for Python programmers.
Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
2. Probabilistic Programming & Bayesian Methods for Hackers
By Cam Davidson-Pilon
An intro to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second point of view.
The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.
3. Understanding Machine Learning: From Theory to Algorithms
By Shai Shalev-Shwartz and Shai Ben-David
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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
4. The Elements of Statistical Learning
By Trevor Hastie, Robert Tibshirani and Jerome Friedman
This book descibes the important ideas in these areas 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 color graphics. It should be 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.
5. An Introduction to Statistical Learning with Applications in R
By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.
6. Foundations of Data Science
By Avrim Blum, John Hopcroft, and Ravindran Kannan
While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems. With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, just as an understanding of automata theory, algorithms, and related topics gave students an advantage in the last 40 years.
7. A Programmer's Guide to Data Mining: The Ancient Art of the Numerati
By Ron Zacharski
This guide follows a learn-by-doing approach. Instead of passively reading the book, I encourage you to work through the exercises and experiment with the Python code I provide. I hope you will be actively involved in trying out and programming data mining techniques. The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques.
8. Mining of Massive Datasets
By Jure Leskovec, Anand Rajaraman and Jeff Ullman
The book is based on Stanford Computer Science course CS246: Mining Massive Datasets (and CS345A: Data Mining).
The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.
9. Deep Learning
By Ian Goodfellow, Yoshua Bengio and Aaron Courville
The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free.
10. Machine Learning Yearning
By Andrew Ng
AI, Machine Learning and Deep Learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:
Historically, the only way to learn how to make these 'strategy' decisions has been a multi-year apprenticeship in a graduate program or company. I am writing a book to help you quickly gain this skill, so that you can become better at building AI systems.
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Abstract
Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications Overview Harness the power of R for statistical computing and data science Use R to apply common machine learning algorithms with real-world applications Prepare, examine, and visualize data for analysis Understand how to choose between machine learning models Packed with clear instructions to explore, forecast, and classify data In Detail Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of 'big data' and 'data science'. Given the growing prominence of Ra cross-platform, zero-cost statistical programming environmentthere has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. 'Machine Learning with R' is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. 'Machine Learning with R' will provide you with the analytical tools you need to quickly gain insight from complex data. What you will learn from this book Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches Use R to prepare data for machine learning Explore and visualize data with R Classify data using nearest neighbor methods Learn about Bayesian methods for classifying data Predict values using decision trees, rules, and support vector machines Forecast numeric values using linear regression Model data using neural networks Find patterns in data using association rules for market basket analysis Group data into clusters for segmentation Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, and big data Approach Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.
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