A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental concepts and algorithms by building on first principles, while also exposing the reader to more advanced topics with extensive pointers to the literature, within a unified notation and mathematical framework.
The material is organized according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, as well as directed and undirected models. This monograph is meant as an entry point for researchers with an engineering background in probability and linear algebra.
Machine Learning from Scratch
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. Each chapter in this book corresponds to a single machine learning method or group of methods. In other words, each chapter focuses on a single tool within the ML toolbox.
Understanding Machine Learning: From Theory to Algorithms
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Advanced R Programming
This book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side
Data Analysis and Prediction Algorithms with R
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts from probability, statistical inference, linear regression and machine learning and R programming skills. Throughout the book we demonstrate how these can help you tackle real-world data analysis challenges.
The R Inferno
An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks.
R is free, open-source, and has thousands of contributed packages.
A Practical Introduction to Python Programming
This book is specifically designed to be used in an introductory programming course, but it is also useful for those with prior programming experience looking to learn Python.
Full Stack Python
Full Stack Python explains important Python concepts in plain language terms without assuming that you already know much about web development, deployments or running an application. Each chapter focuses on a large subject area, such as data or development environments, then digs into concepts and implementations that you should know to make the right choices for what to use when building your projects.
Think of Full Stack Python as your high-level guide to Python development so that you can understand what you do not know and gain a map for what to research next.
Invent Your Own Computer Games With Python
Invent Your Own Computer Games with Python teaches you how to program in the Python language. Each chapter gives you the complete source code for a new game, and then teaches the programming concepts from the examples. Games include Guess the Number, Hangman, Tic Tac Toe, and Reversi. This book also has an introduction to making games with 2D graphics using the Pygame framework.