Machine learning and artificial intelligence are growing fields and growing topics of study.
While the advanced machine learning applications we hear about in the news may sound intimidating and out of reach, the basic concepts are actually quite easy to grasp.
In this post, we’ll review some of the most popular resources for machine learning beginners (or anyone just looking to learn).
Read More Private: Google Launched Free Interview Preparation Course| Enroll Now
1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has driven the entire machine learning field. Now even programmers who know nothing about this technology can use simple and effective tools to implement programs that can learn from data. This practical book will show you how.
Using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools used to build intelligent systems. You will learn a variety of techniques, starting with simple linear regression and ending with deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is a little bit of programming experience to get started.
2. Mathematics for Machine Learning Book by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth
We wrote a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine-learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.
3 . Python Machine Learning By Example – Third Edition By Yuxi (Hayden) Liu
Python Machine Learning With Examples, Third Edition serves as a comprehensive introduction to the world of machine learning (ML).
Featuring six new chapters on topics such as Developing a Film Recommendation Engine with Naive Bayes, Face Recognition with Support Vector Machines, Predicting Stock Prices with Artificial Neural Networks, Categorizing Clothing Images with Convolutional Neural Networks, Predicting with Sequences Using Repetitive Neural Networks, and Leverage Learning for decision making, this book has been significantly updated for the latest business needs.
At the same time, this book offers practical insight into the most important foundations of ML with Python programming. Hayden applies his expertise to demonstrating algorithm implementations in Python, both from scratch and with libraries.
Each chapter goes through industry-recognized applications. Through real-world examples, you will gain an understanding of the mechanics of machine learning techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.
By the end of this ML Python book, you will have a thorough understanding of the ML ecosystem and be familiar with best practices for applying ML techniques to solve problems.
Read More Creating Video Lessons with Online Video Maker InVideo
4. Introduction to Machine Learning with Python By Andreas Muller
Machine learning has become an integral part of many commercial applications and research projects, but the field is not exclusive to large companies with large research teams. If you use Python, even as a beginner, this book will show you practical ways to create your own machine-learning solutions. With all the data available today, machine learning applications are only limited by your imagination.
You will learn the steps necessary to build successful machine-learning applications using Python and the sci-kit-learn library. Authors Andreas Müller and Sara Guido focus on the practical aspects of using machine learning algorithms rather than the math behind them. Familiarity with the NumPy and Matplotlib libraries will help you get more out of this book.
5. The Hundred-Page Machine Learning Book By Andriy Burkov
Everything you really need to know about machine learning in 100 pages!
This book provides a great practical guide to getting ML up and running in a matter of days without having to know much about ML a priori. The first five chapters should be enough to get you started, and the next few chapters will give you a good idea of more advanced topics to pursue. This a great book for engineers looking to integrate ML into their everyday work without having to spend a lot of time pursuing formal graduate programs.
This is a first-read-first-buy-later book of its kind. You can find these books online, read them, then return to pay for them if you like the books or use them for work, business, or find their study useful.
6. Machine Learning for Absolute Beginners By Oliver Theoland
In the age of machine learning, computers don’t need to be given “input commands” to perform tasks, but “inputs”. By entering data, they can make their own decisions and act almost like humans.
But as a machine, it can consider more scenarios and perform calculations to solve complex problems.
This is the element that most excites companies and budding machine learning engineers. The ability to solve complex problems has never been attempted before. This may also be one of the reasons why you might want to purchase this book as an introduction to machine learning for beginners.
7. Python for Data Analytics By Wes McKinney
Python for Data Analytics covers the basics of data manipulation, processing, cleaning, and processing with Python. It’s also a practical, modern introduction to scientific computing in Python, tailored for data-intensive applications. This is a book about the parts of the Python language and the libraries you need to effectively solve various data analysis problems. This book is not a presentation of an analytical method that uses Python as its implementation language.
Written by Wes McKinney, lead author of Pandas Library, this practical book is full of practical case studies. It is ideal for analysts new to Python and Python programmers new to scientific computing.
Today’s Thought
“The successful warrior is the average man, with laser-like focus.” – Bruce Lee
