Breaking News

15 Free Machine Learning and Deep Learning Books – KDnuggets

Image by Editor 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. 

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. 
Click here to read. 

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. 
Click here to read.

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. 
Click here to read. 

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. 
Click here to read. 

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. 
Click here to read. 

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. 
Click here to read. 

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
Click here to read. 

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.
Click here to read. 

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. 
Click here to read. 

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.
Click here to read. 

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.
Click here to read. 

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. 
Click here to read. 

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

Click here to read. 

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.
Click here to read. 

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. 
Click here to read

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. 
More On This Topic
Source: https://news.google.com/__i/rss/rd/articles/CBMiU2h0dHBzOi8vd3d3LmtkbnVnZ2V0cy5jb20vMjAyMi8xMC8xNS1mcmVlLW1hY2hpbmUtbGVhcm5pbmctZGVlcC1sZWFybmluZy1ib29rcy5odG1s0gEA?oc=5