The Best Books for Machine Learning Beginners
By: Christopher Zita
Tags: Programming, Data Science, Deep Learning, Python, Machine Learning

The Best Books for Machine Learning Beginners

4 of the Best Machine Learning books out there and why they will make you the next big Data Scientist.

Machine Learning is the hottest topic currently in the atmosphere of data science. With it’s unique applications, start ups and larger companies are hiring more and more data scientists to implement these models to get a better understanding of their business and their customers.

For anyone that is getting involved in data science, Data Scientist is the career job we all dream of but you have to know your stuff, and that starts with understanding the concepts of Machine Learning!!

Therefore I’m going to give you the 4 best Machine Learning books to read if your a beginner and looking to become a Data Scientist in the future, or just interested to learn more about the topic.

Photo by Kimberly Farmer from Unsplash

1. Introduction to Machine Learning with Python: A Guide for Data Scientists.

If your just getting started with Machine Learning this is a must read. It focuses mostly on the Scikit-Learn library with an in-depth tour of some of the most useful methods in Machine Learning— classifying, regression, a bit of clustering, PCA, and all the different ways to measure the outcome of your model. Introduction to Machine Learning with Python is easy to understand and will explain thoroughly all the necessary steps to create a successful machine-learning application with Python.

The Unanimous book to read for those starting machine learning.

Link: https://amzn.to/3b6ygSZ

2. The Hundred Page Machine Learning Book

The quintessential book for those looking to learn machine learning fast. This book can be read in one night and has all the information you would need to create your own models with machine learning. It is clear, concise, and probably the best machine learning book I've read with respect to number of pages and quality of content.

Link: https://amzn.to/3fqMr8y

3. Python Machine Learning

This is a fantastic introductory book in machine learning with python. It provides enough background about the theory of each (covered) technique followed by its python code. One nice thing about the book is that it starts implementing Neural Networks from the scratch, providing the reader the chance of truly understanding the key underlaying techniques such as back-propagation. Even further, the book presents an efficient (and professional) way of coding in python, key to data science.

I strongly recommend it to those with a moderate level of understanding of machine learning principals and python.

Link: https://amzn.to/2L604fM

4. Hands-On Machine Learning with Scikit-Learn and TensorFlow

The author Aurelien Geron does a great job with explaining different concepts with the prime focus on the practical implementation of Scikit-Learn and TensorFlow. The book is split into two, with the first half covering Scikit-Learn, it is a good mix of practical with theoretical. The Scikit-Learn section is a great reference and has nice detailed explanations with good references for further reading to deepen your knowledge. The second half dives deep into deep learning with TensorFlow, the next step to understanding Machine Learning to its fullest. Deep learning is explained using the easy to learn Keras library with the combination and power from TensorFlow.

Link: https://amzn.to/3fq1foc