The 10 Most Insightful Machine Learning Books You Must Read in 2020 – Analytics Insight

  • Lauren
  • March 9, 2020
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Machine Learning is evidently a vast field and its study is one of the most enlightening tasks one could ever undertake. Today most of the business operations and innovations are done around ML and its innovative applications. A number of professionals are up-skilling themselves with advanced ML knowledge to thrive ahead in their respective fields. They are more keen on learning the offerings, advancements, experts’ opinion and various nuances in context to machine learning or artificial intelligence (AI) as a whole.
If you are tech-enthusiast and looking forward to learning some new ideas and innovations about machine learning, you can find plenty of comprehensive books that demonstrate and offer various skills, advice and learning opportunities. Here is the list of top 10 machine learning books techies should read in 2020.

Machine Learning (in Python and R) For Dummies
Author: John Paul Mueller and Luca Massaron
The book offers advice on installing R on Windows, Linux and macOS platforms, creating matrices, interacting with data frames, working with vectors, performing basic statistical tasks, operating on probabilities, carrying out cross-validation, processing and leveraging data, working with linear models, and the idea behind different algorithms.
The book is aimed at beginners – whether they need to learn how to code in R using RStudio, or code in Python using Anaconda, the book gives a lowdown on Python and R. Authored by two experienced data scientists, the book is a handy guide for key concepts on data analysis, data mining and gives a lowdown on how to leverage common algorithms.

The Hundred-Page Machine Learning Book
Author: Andriy Burkov
Is it possible to explain various ML topics in a mere 100 pages? The Hundred-Page Machine Learning Book by Andriy Burkov is an effort to realize the same. Written in an easy-to-comprehend manner, the ML book is endorsed by reputed thought leaders to the likes of the Director of Research at Google, Peter Norvig and Sujeet Varakhedi, Head of Engineering at eBay.
Post a thorough reading of the book, you will be able to build and appreciate complex AI systems, clear an ML-based interview, and even start your very own ml-based business. The book, however, is not meant for absolute ML beginners. If you’re looking for something more fundamental look somewhere else.
The book covers topics included the anatomy of a learning algorithm, fundamental algorithms, neural networks, and deep learning, other forms of learning, and supervised learning and unsupervised learning.

Machine Learning for Absolute Beginners: A Plain English Introduction
Author: Oliver Theobald
You want to learn ML but have no idea how? Well, before you embark on your epic journey into ML, there are some important theoretical and statistical principles you should know first. And that’s where this book comes in! It is a practical and high-level introduction to ML for absolute beginners.
Machine Learning for Absolute Beginners teaches you everything basic from learning how to download free datasets to the tools and ML libraries you will need. Topics like data scrubbing techniques, regression analysis, clustering, basics of neural networks, bias/variance, decision trees, etc. are also covered. So, if you haven’t had that Lion King moment yet, where you proudly gaze on the expanse of ML-like Simba looks over the Pride Lands of Africa, then this is the best book to gently hoist you up and offer you a clear lay of the land.

Machine Learning for Beginners
Author: Scott Chesterton
A veteran of over half a dozen books on ML, Scott Chesterton brings together the basic aspects of machine learning in this book, such as popular machine learning frameworks being used, ML algorithms, evaluation systems, data mining, and other common applications of machine learning. The book features commentaries on ML software such as TensorFlow, Reptilian, Logstash, Elasticsearch, Installing Marvel, Bro, HDFS, HBASE, Syslog, SNMP, messaging layer and real-time processing layer.
Essentially for beginners, the book covers key concepts such as data preparation, cleaning datasets, classification, testing, induction and deduction, inductive preference, overfitting and under-fitting, and text data extraction. Beginners interested in ML will also be able to learn key algorithms such as Decision Tree, Apriori, DBSCAN, Knowledge Mapping, Linear Models, K-Nearest Neighbors, support vector machine (SVM), FP-Growth and the new wave in ML – neural networks and the popular convolutional neural network algorithms along with their practical applications.

Programming Collective Intelligence: Building Smart Web 2.0 Applications
Author: Toby Segaran
Regarded among the best books to begin understanding ML, the Programming Collective Intelligence by Toby Segaran was written way before, in 2007, data science and ML reached its present status of top career avenues. The book makes use of Python as the vehicle of delivering the knowledge to its readers.
The Programming Collective Intelligence is less of an introduction to ML and more of a guide for implementing ML. The book details on creating efficient ML algorithms for gathering data from applications, creating programs for accessing data from websites, and inferring the gathered data. Each chapter features exercises for extending the stated algorithms and further improve their efficiency and effectiveness.
The topics covered in this book are bayesian filtering, collaborative filtering techniques, evolving intelligence for problem-solving, methods for detecting groups or patterns, non-negative matrix factorization, search engine algorithms, support vector machines, and ways to make predictions.

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)
Authors: Drew Conway & John Myles
In case you are a programmer now interested in data crunching, then this book is perfect for you! (Lets first clarify that the Hacker in the title refers to a good programmer and not a secretive computer cracker!) So this book will help you get started with ML using lots of hands-on case studies rather than boring math-heavy presentations that are more common.
Machine Learning for Hackers focuses on specific problems in each chapter such as classification, prediction, optimization, and recommendation. It will also teach you to analyze different sample datasets and write simple ML algorithms in the R programming language.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Author: Aurelien Geron
One of the most-read books in Artificial Intelligence and ML space, this handy guide by Aurelein Geron is a must-read for data scientists and ML enthusiasts looking for practical examples on how to implement ML tools.
The book is aimed at readers who have Python coding experience. The book introduces readers to a number of techniques, ranging from simple linear regression to deep neural networks, for building intelligent systems on popular Python frameworks such as Scikit-Learn, Keras and TensorFlow. Readers can also learn how to train models such as support vector machines, decision trees, random forests, and ensemble methods work. The major prerequisite before you buy this book is to have a background in Python.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Author: Trevor Hastie, Robert Tibshirani, and Jerome Friedman
If you like statistics and want to learn machine learning from the perspective of stats then The Elements of Statistical Learning is the book that you must read. The ML book emphasizes mathematical derivations for defining the underlying logic of an ML algorithm. Before picking up this book, ensure that you have at least a basic understanding of linear algebra.
The concepts explained in The Elements of Statistical Learning book aren’t beginner-friendly. Hence, you might find it complex to digest. If you still, however, want to learn them then you can check out the An Introduction to Statistical Learning book. It explains the same concepts but in a beginner-friendly way.
The topics covered in this book are Ensemble learning, High-dimensional problems, Linear methods for classification and regression, Model inference and averaging, Neural networks, Random forests, and Supervised and unsupervised learning.

Machine Learning: The New AI (The MIT Press Essential Knowledge Series)
Author: Ethem Alpaydin
ML has an insane range of applications in modern times, from product recommendations to voice recognition and even those that are not commonly used like self-driving cars! Now, the basis of ML is data and as data has grown bigger (Big data!), it is no surprise that ML has also advanced as it is fundamental in the process of converting data into knowledge.
Machine Learning: The New AI focuses on basic ML, ranging from the evolution to important learning algorithms and their example applications. This book also focuses on machine learning algorithms for pattern recognition; artificial neural networks, reinforcement learning, data science and the ethical and legal implications of ML for data privacy and security

Artificial Intelligence and Machine Learning for Business
Author: Scott Chesterton
This book by Scott Chesterton is not a long read or may not contain advanced coding examples, but acts as a good theoretical resource on how to operationalize AI and ML projects, how ML tools and techniques can be best utilized to process big data, and how to visualize a predictive model’s analytical results. The book is aimed at intermediate-level users who are familiar with ML tools, frameworks, and techniques.
This book will be most useful for ML engineers and analytics managers at organizations who are looking to develop new AI and ML projects to spur business growth or to build their enterprise strategy. Through this book, Chesterton introduces readers to machine learning projects and how they can be used to improve an organization’s capabilities and competitiveness and how ML teams can prepare for new challenges when deploying machine learning at scale.