Go on the complete R journey from tidying your data, whether small,
complex, or big, to implementing and evaluating a variety of
machine learning models Key Features * The 10th Anniversary Edition
of the bestselling R machine learning book, updated with 50% new
content for R 4.0.0 and beyond * Harness the power of R to build
flexible, effective, and transparent machine learning models *
Learn quickly with a clear, hands-on guide by machine learning
expert Brett Lantz Book 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. Machine Learning with R, Fourth
Edition 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 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 ML in the last few years and help you build
your data science skills and tackle more challenging problems,
including making successful ML models and advanced data
preparation, building better learners, and making use of big data.
You'll also find updates to the classic R data science book to R
4.0.0 with newer and better libraries, advice on ethical and bias
issues in machine learning, and an introduction to deep learning.
Whether you're looking to take your first steps with R for machine
learning or making sure your skills and knowledge are up to date,
this is an unmissable read. Find powerful new insights in your
data; discover machine learning with R. 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 Who
This Book Is For Data scientists, actuaries, data analysts,
financial analysts, social scientists, business and machine
learning students, and other practitioners who want a clear,
accessible guide to machine learning with R. No R experience is
required, although prior exposure to statistics and programming is
helpful.
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