16 Machine Learning
16.1 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!
16.2 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.
16.3 Interpretable Machine Learning
A Guide for Making Black Box Models Explainable
Pay what you want for the ebook, minimum $0.00
16.4 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
16.5 Supervised Machine Learning for Text Analysis in R
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.
16.6 Lightweight Machine Learning Classics with R
Marek Gagolewski
In this book we will take an unpretentious glance at the most fundamental algorithms that have stood the test of time and which form the basis for state-of-the-art solutions of modern AI, which is principally (big) data-driven.
16.7 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.
16.8 Mathematics and Programming for Machine Learning with R: From the Ground Up 1st Edition, Kindle
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.
16.9 The caret Package
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.
16.10 A Minimal rTorch Book
Alfonso R. Reyes
Practically, you can do everything you could with PyTorch within the R ecosystem.
16.11 Model-Based Clustering and Classification for Data Science
Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, and Adrian E. Raftery
Among the broad field of statistical and machine learning, model-based techniques for clustering and classification have a central position for anyone interested in exploiting those data. This text book focuses on the recent developments in model-based clustering and classification while providing a comprehensive introduction to the field. It is aimed at advanced undergraduates, graduates or first year PhD students in data science, as well as researchers and practitioners.
16.12 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).
16.13 mlr3 book
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/