Machine learning got one of the hottest spaces in Computer Science. Every organization is applying Machine Learning to solve their issues and comprehend their data sets. A good method to start is to find and read a book.
Best Machine Learning Books List
Machine learning is a complex field but if you have access to the right resources it can be studied in an easy way. To help you improve your ML knowledge here is the list of the best Machine Learning Books:
Machine Learning for Absolute Beginners
Author – Oliver Theobald
Publisher – Scatterplot Press
Book link : click here
If you don’t have previous experience in machine learning do not miss out on this book. As no coding and mathematical background are required to start learning from this book.
The author starts describing what machine learning is, which are the techniques and algorithms, and then lists the future of & resources for machine learning learners. In order to help the readers to understand every topic in the book, explanations and examples are included with various algorithms.
Contents of the book:
- Artificial Intelligence
- Big Data
- Downloading Free Datasets
- Regression
- Support Vector Machine Algorithms
- Deep Learning/Neural Networks
- Data Reduction
- Clustering
- Association Analysis
- Decision Trees
- Recommenders
- Machine Learning Careers
The Hundred-Page Machine Learning Book
Author – Andriy Burkov
Publisher – Andriy Burkov
This is a great book for an introduction to the field for those with technical backgrounds in math, science, or computer science.
That is to say that this book can be also a reference for those who aren’t up to date in this field.
With this book, you will learn to build and understand complex AI systems, hold ML-based interviews, and even start your own ml-based business.
However, considering the contents, the book is not meant for absolute machine learning beginners.
Contents of the book:
- Fundamental learning algorithms
- Neural networks
- Deep Learning
- Supervised learning and unsupervised learning
- Other forms of learning
Machine Learning for Hackers
Author – Drew Conway and John Myles White
Publisher – O’Reilly Media
Machine Learning for Hackers is for the experienced programmer interested in working with data. The word hackers refer to fact that the book is for experts in mathematics. It is an excellent option for those with a good knowledge of R as most of the book is based on data analysis in R. For example it also explains advanced data wrangling using R.
Topics covered by the book:
- Data Exploration
- Classification Spam filtering
- Regression
- Unsupervised learning
- Social graph analysis
Machine Learning with TensorFlow
Author – Nishant Shukla
Publisher – Manning Publications
TensorFlow is an open-source Machine Learning platform, and one of the most used and a leading player in the data science space.
The book gives to the readers an introduction to machine learning using notions and practical coding examples (the code is available on Github).
Firstly, the book explains the basics of traditional classification, clustering, and prediction algorithms.
Secondly, it dives into deep learning allowing the readers to be ready for any kind of machine learning project with the TensorFlow library.
Topics covered by the book:
- Clustering data.
- Autoencoders.
- Convolutional, recurrent, reinforcement neural networks.
- Deep learning.
- Hidden Markov models.
- Reinforcement learning.
- Neural networks.
- Seq2Seq model.
- Logistic regression, Linear regression.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author – Aurélien Géron
Publisher – O’Reilly Media
Book link : click here
The Hands-On Machine Learning book gives an intuitive explanation of the various concepts and tools that you need to develop smart, intelligent machine learning based systems.
Programming skills are needed to get started with this Machine Learning book as the book contains various exercises that will help the reader apply what learned during the reading of the book.
Topics covered by this book:
- Deep neural networks
- Deep reinforcement learning
- Linear regression
- Classification
- Training Models
- Support Vector Machines
- Decision Trees
- Ensemble Learning and Random Forests
- Dimensionality Reduction
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Author – Aurélien Géron
Publisher – O’Reilly Media
Book link : click here
The Hands-On Machine Learning book gives you a hands-on set of concepts and tools to build a project from the beginning to the end.
This book is not only for technical people as it is centered also around the lessons that the author learned during his job as a Machine Learning engineer.
Topics covered by this book:
- Deep neural networks
- Deep reinforcement learning
- Linear regression
- Classification
- Training Models
- Support Vector Machines
- Decision Trees
- Ensemble Learning and Random Forests
- Dimensionality Reduction