Book Image

Mastering Machine Learning with R - Third Edition

By : Cory Lesmeister
Book Image

Mastering Machine Learning with R - Third Edition

By: Cory Lesmeister

Overview of this book

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models. This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you’ll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you’ll get a glimpse into how some of these blackbox models can be diagnosed and understood. By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.
Table of Contents (16 chapters)

Time Series and Causality

"An economist is an expert who will know tomorrow why the things he predicted yesterday didn't happen today."
- Laurence J. Peter

A univariate time series is where the measurements are collected over a standard measure of time, which could be by the minute, hour, day, week, month, and so on. What makes the time series problematic over other data is that the order of the observations matters. This dependency of order can cause standard analysis methods to produce an unnecessarily high bias or variance.

It seems that there's a paucity of literature on machine learning and time series data or it's substandard. For example, I was at a data science conference in the spring of 2018, and a highly regarded machine learning expert mentioned that vector autoregression requires the data to be stationary. We'll discuss this later. When...