15 Free Machine Learning and Deep Learning Books
Check out this list of 15 FREE ebooks for learning machine learning and deep learning.
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If you’re looking to have a career in machine learning or a data scientist who wants to transition into the machine learning world, below is a list of FREE e-books to help you achieve this.
Understanding Machine Learning: From Theory to Algorithms
By Shai Shalev-Shwartz and Shai Ben-David
This book is split into 4 parts: Part I: Foundations, Part II: From Theory to Algorithms, Part III: Additional Learning Models, and Part IV: Advanced Theory. If you would like to see the content, click here.
Think Stats: Probability and Statistics for Programmers
by Allen B. Downey
If you already have a basic understanding of Python and can apply it, you can further apply these skills and better understand the concepts of probability and statistics. It goes into depth and will take your Machine Learning journey to the next level.
An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani
Statistics is a major part of Machine Learning, so the more you know the better. If you’re looking for another book to help you grasp statistics - this book goes into Deep learning, Survival analysis, Multiple testing, and more.
Machine Learning for Humans
By Vishal Maini Samer Sabri
Another great book for beginners. If you’re new to Machine Learning and want to increase your knowledge of its foundations - this is a valuable ebook. You will be going through Supervised Learning, Unsupervised Learning, Neural Networks and Deep Learning, Reinforcement Learning, and the best machine learning resources.
Mining of Massive Datasets
By Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman
We have a lot of data available, the more we have available - the bigger the dataset becomes. Being able to mine massive datasets and derive valuable insight that can be further used for the decision-making process is a skill that is becoming very popular.
Machine Learning Engineering
By Andriy Burkov
If you’re looking for a career, particularly as a Machine Learning Engineer - this book is for you. It is broken down into 9 chapters: Introduction, Before the Project Starts, Data Collection and Preparation, Feature Engineering, Supervised Model Training (Part 1), Supervised Model Training (Part 2), Model Evaluation, Model Deployment, Model Serving, Monitoring, and Maintenance, and the Conclusion. This is the ultimate breakdown of a machine learning engineer's day-to-day life.
The Hundred-Page Machine Learning Book
By Andriy Burkov
Burkov also has another machine learning book, however, this one goes through the foundations of the sector and then moves onto more advanced practices such as Notation and Definitions, Fundamental Algorithms plus in-depth material, Anatomy of a Learning Algorithm, Basic Practice, Neural Networks, and Deep Learning, Problems and Solutions, Advanced Practice
Mathematics for Machine Learning
By Marc Peter Deisenroth A. Aldo Faisal Cheng Soon Ong
We can never forget how important Mathematics is in Machine Learning. It is one of the areas that take a lot of time to grasp. In this book, you will cover Linear Algebra, Analytic Geometry, Matrix Decomposition, Linear Regression, Dimensionality Reduction with PCA, Density Estimation, and Classification with Support Vector Machines.
Feature Engineering and Selection: A Practical Approach for Predictive Models
By Max Kuhn and Kjell Johnson
Feature Engineering is an important element of machine learning models. This ebook guides you on the correct practices of feature engineering and predictive modeling. The topics covered are Predictive modeling using an example, measuring performance, tuning parameters, model optimization, exploratory visualization, and more.
Pattern Recognition and Machine Learning
By Christopher M Bishop
This 758-page e-book goes through a lot! You will first be introduced to probability and its distribution extensively. You will then move into linear models for regression and classification, and then further onto neural networks and other topics such as kernel methods, etc.
Hands-On Machine Learning with R
By Bradley Boehmke & Brandon Greenwell
If R is your chosen programming language and you’ve started to dive into Machine Learning - this is the book for you. It covers the most common machine learning methods such as Generalized low-rank models, Clustering algorithms, Autoencoders, Regularized models, Random forests, Gradient boosting machines, Deep neural networks, Stacking / super learners, and more.
An Introduction to Machine Learning Interpretability
By Patrick Hall and Navdeep Gill
As a machine learning engineer, there may be a time when you will have to explain your model. Executives do not typically have a tech background, therefore being able to interpret and explain your AI to people like this is a very important skill and gets you very far.
Natural Language Processing with Python
By Steven Bird, Ewan Klein, and Edward Loper
If you have an interest in Natural Language Processing and you are proficient in Python - this book is for you. You will go over:
- Language Processing and Python
- Accessing Text Corpora and Lexical Resources
- Processing Raw Text
- Writing Structured Programs
- Categorizing and Tagging Words
- Learning to Classify Text
- Extracting Information from Text
- Analyzing Sentence Structure
- Building Feature Based Grammars
- Analyzing the Meaning of Sentences
- Managing Linguistic Data
- Afterword: Facing the Language Challenge
Python Machine Learning Projects
By Brian Bocheron and Lisa Tagliaferri
You’re probably at the point where you want to create machine-learning projects to test your skills and build a portfolio. Projects are a major element of your career in the tech industry and are imperative to help you land a job.
Introduction to Machine Learning Interviews Book
by Chip Huyen
If you’re at a point where you have a good understanding of Machine Learning under your belt and you’re ready to start applying for jobs - knowing which kind of interview questions you will be up against is handy. You will get a better understanding of the different types of roles, companies, and the interview pipeline.
Conclusion
I hope this article has helped you gather free resources to help build your knowledge in machine learning and kickstart your career.
Look out for the next list of free machine learning and deep learning ebooks!
Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.
