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Machine Learning with R: building and improving machine learning models
Textbook
Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data, 4th Edition
Author(s): Brett Lantz
Course Description
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
This Course provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data.
What you will learn
Learn the end-to-end process of machine learning from raw data to implementation
Classify important outcomes using nearest neighbor and Bayesian methods
Predict future events using decision trees, rules, and support vector machines
Forecast numeric data and estimate financial values using regression methods
Model complex processes with artificial neural networks
Prepare, transform, and clean data using the tidyverse
Evaluate your models and improve their performance
Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow
Machine Learning with R building and improving machine learning models.pdf
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