Hands-On Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory.
Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results.
- Offers a practical and applied introduction to the most popular machine learning methods.
- Topics covered include feature engineering, resampling, deep learning and more.
- Uses a hands-on approach and real world data.
Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc. can be a painstakenly laborious process. In fact, its been stated that up to 80% of data analysis is spent on the process of cleaning and preparing data. However, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it’s essential that you become fluent and efficient in data wrangling techniques. Data Wrangling with R! will help you learn the essentials of preprocessing data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information.
This book will guide you through the data wrangling process along with give you a solid foundation of working with data in R. I teach you how to easily wrangle your data, so you can spend more time focused on understanding the content of your data via visualization, analysis, and reporting. By the time you finish reading this book, you will have learned:
- How to work with the different types of data such as numerics, characters, regular expressions, factors, and dates
- The difference between the different data structures and how to create, add additional components to, and how to subset each data structure
- How to acquire and parse data from locations you may not have been able to access before such as web scraping
- How to develop your own functions and use loop control structures to reduce code redundancy
- How to use pipe operators to simplify your code and make it more readable
- How to reshape the layout of your data, and manipulate, summarize, and join data sets
- Not only will you learn many base R functions, you’ll also learn how to use some of the latest data wrangling packages such as
In essence, you will have the data wrangling toolbox required for modern day data analysis. I hope you enjoy and please provide any feedback so I can continue modifying and improving future versions.