Machine Learning books for beginner, intermediate and advanced learners.
We are pleasure driven by the sense of smell and the aura of a new book is like no other. In the world of kindle and wattpads, paperback still remains among the premium choice.
To tingle your ‘smell’ buds, here is a curated list of Machine Learning books for beginner, intermediate and advanced learners.
‘A Journey of a Thousand Miles Begins With a Single Step’
So let us start with the Machine Learning Books For Beginners .
Machine Learning For Dummies.
Authors : John Paul Mueller and Luca Massaron.
Fed up of giving ‘yet another try’ at understanding machine learning? Well here is an anthology to your rescue. This truly is the guide for those who find it impossible to understand machine learning. The book uses Python codes – but wait, you don’t really have to know Python to understand all of it. The authors have thoughtfully explained the minute details with great ease. A little bit of math, a pinch of logic and a will to learn and you are good to go, with no hardcore coding expertise required.
Author : Stephen Marsland
The book is aimed at computer science and engineering undergraduates studying machine learning and artificial intelligence. All the codes as well as the datasets are available online for free. The book is Published by CRC press, written by Stephen Marsland and is a great source for Python learning.
You can read about the Author here.
Building Machine Learning Systems with Python
Authors : Willi Richert and Luis Pedro Coelho.
Here is a hands-on guide on Machine Learning by Willi Richert and Luis Pedro Coelho. The book talks about Machine Learning using Python and is specifically aimed at those taking toddler steps in Machine Learning.
You can download the pdf here.
Chin up warrior, we are just halfway through!
So you say you know the basics well but you haven’t mastered the concepts yet. Well these books will help you tighten your grip just well – Hardcovers for Intermediates.
Author : Peter Flach.
The book covers practical examples of machine learning in action to dig in deeper into machine learning. The human brain tends to forget. So this book is perfect for Intermediate-to-advanced developers who want a quick snippet of the basics. You will learn about statistical models – generating, analyzing and prediction using machine learning techniques neatly explored with graphs, charts and diagrams. A little knowledge of data science or machine learning is the only prerequisite.
Author : Sebastian Raschka
This is among the best guides for Python developers if you wish to dig deeper into machine learning. Python Machine Learning dives really deep into Scikit-learn and how to use it for data analysis. The author recommends visualization to be done simultaneously with algorithm so that you can learn not only how to compute data but visualize it as well. A decent background of Python and a wee-bit experience with scikit-learn will certainly accentuate your understanding of the concepts covered in the book.
Data Science from Scratch
Author : Joel Grus
One more Python based learning book – Data Science from Scratch covers the concepts with much more detailed examples. Every code has an intro to Python which makes it easier to understand the concepts thoroughly. To compare with Python Machine Learning, Data Science from Scratch does not delve in as deeper as the prior and the later one has a very clear and precise writing style so both are equally good. The most engaging point of this book is the interconnected coding style.
Make Your Own Neural Network
Author : Tariq Rashid
Now before you wonder why is a book on neural networks mentioned in the list, Make your own neural network also teaches you how to build your algorithms in Python. According to the author, neural networks are a fundamental component of machine learning and his book justifies it. Yes, you do need knowledge of Python to understand the concepts well. Neural networks are hard to get along with, but with his gentle and easy to grasp writing style, Tariq Rashid’s Make Your Own Neural Network is all you need.
‘Every Champion Was Once a Beginner Who Didn’t Quit’
So let us move towards our final segment – Machine Learning Books for Advanced Learners.
Fundamentals of Machine Learning for Predictive Data Analytics
Author : John D. Kelleher, Brian Mac Namee and Aoife D’Arcy
A mouthful name and multiple authors – looks like the perfect recipe for a complex book, isn’t it?
Machine learning is incessantly used for building predictive models by establishing patterns from long datasets. Fundamentals of Machine Learning for Predictive Data Analytics is focussed on such models used in predictive analysis, covering both theoretical and practical concepts. Published by MIT press, the book covers complex probability based machine learning and advanced concepts of developing and analysis of data. The catch is simplicity and specificity of the language which makes it worth its price. One needs a strong mathematical and coding background to enjoy this book.
Pattern Recognition and Machine Learning
Author : Christopher M. Bishop
Now that you are here, here is the competitor to Fundamentals of Machine Learning for Predictive Data Analytics. Pattern Recognition and Machine Learning by Christopher M. Bishop is highly recommended explicitly for advanced data scientists and developers. Every chapter contains topics on probability and machine learning based with exponentially increasing depth in the text. You start off with general introduction and dive in deeper into live examples using a very basic idea to make the point clear. This book is among the best resources to master the concepts. A heavy mathematical background and knowledge of data science is a must to glide your way through. As daunting as it may sound, the writing style and examples in the book will walk you through with ease.
Advanced Data Analytics Using Python
Author : Sayan Mukhopadhyay
This book highlights the concepts of data analytics using Python codes. Once you read this book, you will be familiar with the technical aspects of an analytics project. You’ll get to know the concepts using Python code, which you can implement in your own projects as well. Advanced Data Analytics Using Python discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. The book covers examples of supervised, unsupervised, semi-supervised and deep learning and techniques like classification, clustering, regression and forecasting.
Mastery comes when there is absence of giving up!
So, here is a list of books you can refer to build, hone and master your machine learning concepts. Feel free to share the names of books which you think deserve to be on the list.