15 Machine Learning

15.1 Hands-On Machine Learning with R

Bradley Boehmke & Brandon Greenwell

This book provides hands-on modules for many of the most common machine learning methods to include:

Generalized low rank models, Clustering algorithms, Autoencoders, Regularized models, Random forests, Gradient boosting machines, Deep neural networks, Stacking / super learners and more!


15.2 Feature Engineering and Selection: A Practical Approach for Predictive Models

Max Kuhn and Kjell Johnson

The goals of Feature Engineering and Selection are to provide tools for re-representing predictors, to place these tools in the context of a good predictive modeling framework, and to convey our experience of utilizing these tools in practice.


15.3 Interpretable Machine Learning

Christoph Molnar

A Guide for Making Black Box Models Explainable

Online book

Pay what you want for the ebook, minimum $0.00

Leanpub PDF

15.4 Explanatory Model Analysis

Responsible, Fair and Explainable Predictive Modeling with examples in R and Python

Przemyslaw Biecek, Tomasz Burzykowski

Free online: https://pbiecek.github.io/ema/

Or paid printed: https://www.routledge.com/Explanatory-Model-Analysis-Explore-Explain-and-Examine-Predictive-Models/Biecek-Burzykowski/p/book/9780367135591

15.5 Supervised Machine Learning for Text Analysis in R

Emil Hvitfeldt, Julia Silge

Modeling as a statistical practice can encompass a wide variety of activities. This book focuses on supervised or predictive modeling for text, using text data to make predictions about the world around us. We use the tidymodels framework for modeling, a consistent and flexible collection of R packages developed to encourage good statistical practice.


15.6 Machine Learning for Factor Investing

Guillaume Coqueret and Tony Guida

This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.


15.7 Mathematics and Programming for Machine Learning with R: From the Ground Up 1st Edition, Kindle

William B. Claster

Based on the author’s experience in teaching data science for more than 10 years, Mathematics and Programming for Machine Learning with R: From the Ground Up reveals how machine learning algorithms do their magic and explains how these algorithms can be implemented in code. It is designed to provide readers with an understanding of the reasoning behind machine learning algorithms as well as how to program them. Written for novice programmers, the book progresses step-by-step, providing the coding skills needed to implement machine learning algorithms in R.

Paid ~$40 https://www.amazon.com/Mathematics-Programming-Machine-Learning-Ground-ebook-dp-B08JHDCX9Y/dp/B08JHDCX9Y/ref=mt_other?_encoding=UTF8&me=&qid=1610095251

15.8 The caret Package

Max Kuhn

The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.

This book is free online.


15.9 A Minimal rTorch Book

Alfonso R. Reyes

Practically, you can do everything you could with PyTorch within the R ecosystem.


15.10 Tidy Modeling with R

Max Kuhn and Julia Silge

This book provides an introduction to how to use the tidymodels suite of packages to create models using a tidyverse approach and encourages good methodology and statistical practice throughout demonstrated using series of applied examples.

This book is free online (currently a work in progress).


15.11 mlr3 book

Michel Lang

The mlr3 package and ecosystem provide a generic, object-oriented, and extensible framework for classification, regression, survival analysis, and other machine learning tasks for the R language. They do not implement any learners, but provide a unified interface to many existing learners in R.

The book is free and work in progress. https://mlr3book.mlr-org.com/