by Alfonso R. Reyes
Practically, you can do everything you could with PyTorch within the R ecosystem.
by Enrique Garcia Ceja
This book aims to provide an introduction to machine learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform several of the tasks involved during a data analysis pipeline such as: data exploration, visualization, preprocessing, representation, model training/validation, and so on. All of this, using the R programming language and real-life datasets.
by Przemyslaw Biecek, Tomasz Burzykowski
Responsible, Fair and Explainable Predictive Modeling with examples in R and Python
by Max Kuhn, 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.
by 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!
A Guide for Making Black Box Models Explainable
Paid: Free or pay what you want $42
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.
This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.
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.
by 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.
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.
by 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.
by Przemyslaw Biecek, Anna Kozak, Aleksander Zawada
A graphic novel approach to responsible machine learning
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.
Created and maintained by Oscar Baruffa
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