11 Machine Learning
11.1 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!
11.2 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.
11.3 Interpretable Machine Learning
A Guide for Making Black Box Models Explainable
Pay what you want for the ebook, minimum $0.00
11.4 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.
11.5 Machine Learning for Factor Investing
This book is intended to cover some advanced modelling techniques applied to equity investment strategies that are built on firm characteristics.