More often than not people prefer learning from online courses to books when it comes to machine learning. But reading machine learning books for beginners, at least, will catapult them in the right direction of learning and will help them understand machine learning better than any online course. The simple fact that can back this argument is, that online courses show the practical side of machine learning whereas a book can help a learner get almost exhaustive knowledge about machine learning models with scope for further learning also being there.
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Why should you read machine learning books for beginners?
Machine Learning courses all over the world have essential credentials with them that students can earn upon completion of these courses. They also come as a set of specializations where the students can learn compiled skills in a span of a couple of weeks.
While that is definitely the way to go if you want to get into the Data Science world, reading machine learning books for beginners who are starting out on their journey, gives them a better foundation, even if it is just referring to whatever they learned in courses to these books, they can get a college-level education sitting in the comfort of their homes.
The books come with examples, further readings, and sometimes, highly valuable lab sessions to demonstrate how machine learning can be performed. This might seem similar to an online course but any book on a certain topic well and beyond exceeds any online course on the same. (provided we choose the right books)
We’ll go over 5 such books (with or without multiple parts) that you can read to start off on your journey with the right steps.
List of Simple Machine Learning Books for Beginners:
Below is a brief description of the different machine learning books for beginners:
Making Sense of Data I & II
The book is true to its title and does do justice to it by including almost all things necessary about descriptive and diagnostic analytics with a touch upon Predictive analytics where required to ensure that the readers are given a holistic view of what can be understood out of raw data whilst showing the demonstrations of methods on numerous examples as well. This is not exclusively for just machine learning but it also helps one learn the math behind it and understand how exactly machine learning algorithms go about data and make predictions. It is the first book to start with if you want to read one of the machine learning books for beginners.
Find out more about the book here: Making Sense of Data I & for the second part: Making Sense of Data II
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands on Machine Learning is one of the most cited machine learning books for beginners by renowned programmers around the world just because the book is so comprehensive and exhaustive. It focuses mostly on the Scikit Learn library but as someone starting out in ML, if you use Python the book is going to be as good as the holy grail to you. The official website tells you about the book accurately, it covers the following:
- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
Find out more about the book here: Hands on Machine Learning with Scikit Learn
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
If your work focuses mostly on Python and how you can use it in your work with machine learning, The machine learning books for beginners would include Python for Data Analysis for you. Because no matter how well you know the algorithms, its no use if you cannot spend your time in your analysis properly. Knowing how to wrangle your data and clean it before you go into a predictive algorithm is as or more important than the algorithm itself. The contents of this book include:
- Use high-performance tools to load, clean, transform, merge, and reshape data
- Create scatter plots and static or interactive visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Measure data by points in time, whether it’s specific instances, fixed periods, or intervals
- Learn how to solve problems in web analytics, social sciences, finance, and economics,
Find out more about the book here: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
An Introduction to Statistical Learning
ISLR or an Introduction to Statistical Learning is also a part of our machine learning books for beginnners because it extensively covers all that you need to know about Statistical Analysis in your work and can always teach students more about the math behind machine learning alogrithms and this book comes with lab sessions to show the algorithms working and also what they represent. The book also compares different algorithms with other algorithms.
Find out more about the book here: An Introduction to Statistical Learning
Machine Learning for Dummies
The book machine Learning for dummies is equivalent to what the Machine Learning books for beginners stands for. This book is pretty much all you need if you don’t have any inclination towards Machine Learning. The book starts with introducing how machines learn in machine learning, preparing the learning tools, installing them in your system, getting done with basic maths, learning from smart data and big data, applying learning to real problems, and ten learning packages and methods to improve your machine learning algorithms.
Find out more about the book here: Machine Learning For Dummies
Hopefully, in this article, from machine learning books for beginners, you find the motivation you need to try out machine learning books along with some online courses. We don’t necessarily want you to think that the online courses are not as helpful but they are very limited in terms of information and if you want to learn more about anything, you can just read a book about the topic that you want to learn about and you can definitely learn more than the online course there. The books are not exhaustive and for data science you can read similar books relating to other tools and programming languages.
Machine Learning is considered a black box concept in the world. To learn more about it, books are no doubt, supposed to be a part of it.
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