10 Free Must-Read Machine Learning E-Books For Machine Learning
Machine learning is a use of Artificial Intelligence that gives a system a capacity to naturally take in and enhance from experiences without being unequivocally modified.
Below I have listed some of the best machine learning books for beginners freely available online (in pdf format) to download and kick start Machine Learning Basics for developers to become good at building AI systems quickly.
1.) Understanding Machine Learning: From Theory to Algorithms – By Shai Shalev-Shwartz and Shai Ben-David
Machine learning is one of the quickest developing ranges of software engineering, with expansive applications. This book presents machine learning, and the algorithmic standards it offers, principledly.
The book gives a hypothetical record of the basics basic machine learning and the numerical deductions that change these standards into useful calculations. This book covers critical algorithmic standards including stochastic slope plunge, neural systems, and organized yield learning; and developing hypothetical ideas.
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2.) Machine Learning Yearning – By Andrew Ng
AI, Machine Learning and Deep Learning are changing various enterprises. This book rapidly pick up with the goal that you can turn out to be better at building AI frameworks.
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3.) Think Stats: Probability and Statistics for Programmers – By Allen B. Downey
Think Stats is a prologue to Probability and Statistics for Python developers.
Think Stats accentuates basic strategies you can use to investigate genuine informational collections and answer intriguing inquiries.
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4.) Probabilistic Programming and Bayesian Methods for Hackers – By Cam Davidson-Pilon
An introduction to Bayesian strategies and probabilistic programming from a calculation to start with, arithmetic second perspective.
The Bayesian strategy is the normal way to deal with inference, yet it is avoided perusers behind sections of moderate, numerical examination. The regular content on Bayesian surmising includes a few sections on likelihood hypothesis, then enters what Bayesian derivation is.
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5.) The Elements of Statistical Learning – By Trevor Hastie, Robert Tibshirani and Jerome Friedman
The book’s scope is expansive, from administered learning (expectation) to unsupervised learning.
The numerous points incorporate neural systems, bolster vector machines, characterization trees and boosting- – the primary extensive treatment of this theme in any book.
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6.) Foundations of Data Science – By Avrim Blum, John Hopcroft, and Ravindran Kannan
This book to cover the hypothesis prone to be helpful in the following 40 years, similarly as a comprehension of automata hypothesis, calculations, and related themes gave understudies favorable position over the most recent 40 years.
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7. An Introduction to Statistical Learning with Applications in R – By Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
The book contains various R labs with itemized clarifications on the most proficient method to actualize the different strategies, all things considered, settings, and ought to be an important asset for a rehearsing information researcher.
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8.) A Programmer’s Guide to Data Mining: The Ancient Art of the Numerati – By Ron Zacharski
The reading material is laid out as a progression of little strides that expand on each other until, when you finish the book, you have established the framework for understanding information mining systems.
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9.) Deep Learning – By Ian Goodfellow, Yoshua Bengio and Aaron Courville
The Deep Learning course reading is an asset proposed to help understudies and professionals enter the field of machine learning by and large and profound learning specifically.
The online adaptation of the book is presently total and will stay accessible online for nothing.
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10.) Mining of Massive Datasets – By Jure Leskovec, Anand Rajaraman and Jeff Ullman
The book is outlined at the undergrad software engineering level to bolster further investigations, the majority of the parts are supplemented with further perusing references.
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Further Reading 25 Free eBooks on Machine Learning which might interest you:
1.) Introduction to Machine Learning
3.) Machine Learning – The Complete Guide
4.) Forecasting: principles and practice. Hyndman, Athanasopoulos
5.) Neural Networks and Deep Learning
6.) Bayesian Reasoning and Machine Learning
7.) Information Theory, Inference, and Learning Algorithms
8.) Supervised Sequence Labelling with Recurrent Neural Networks
9.) Reinforcement Learning: An Introduction
10.) A BRIEF INTRODUCTION TO NEURAL NETWORKS
11.) A COURSE IN MACHINE LEARNING
12.) A FIRST ENCOUNTER WITH MACHINE LEARNING
13.) GAUSSIAN PROCESSES FOR MACHINE LEARNING
14.) LEARNING DEEP ARCHITECTURES FOR AI
15.) MACHINE LEARNING, NEURAL AND STATISTICAL CLASSIFICATION
16.) PROBABILISTIC MODELS IN THE STUDY OF LANGUAGE
17.) Machine Learning Evaluation: A Classification Perspective
18.)Introduction to Machine Learning in Python with scikit-learn
19.) The LION Way: Machine Learning plus Intelligent Optimization – Roberto Battiti, Mauro Brunato
20.) Data Mining: Practical Machine Learning Tools and Techniques
21.) Machine Learning, Neural and Statistical Classification; Michie & Spiegelhalter
22.) Machine Learning; Mellouk & Chebira
23.) A First Encounter with Machine Learning – Max Welling
24.) Practical Artificial Intelligence Programming With Java
25.) Machine Learning – The Art & Science of Algorithms that Make Sense of Data – Peter Flach
PS: Download all these Free eBooks now and start learning Machine Learning.
Related:
HOW TO LEARN MACHINE LEARNING IN 90 DAYS
6 Easy Steps To Get Started Learning Artificial Intelligence
A Complete Guide on Getting Started with Deep Learning
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Already Free eBooks on Machine Learning are available… Gr8