Use R and tidyverse to prepare, clean, import, visualize, transform, program, communicate, predict and model data

No R experience is required, although prior exposure to statistics and programming is helpful

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features:

- Get to grips with the tidyverse, challenging data, and big data

- Create clear and concise data and model visualizations that effectively communicate results to stakeholders

- Solve a variety of problems using regression, ensemble methods, clustering, deep learning, probabilistic models, and more

Book Description:

Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic.

Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering.

With three new chapters on data, you'll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights.

Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you'll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques.

Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.

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:

This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any 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.

Solve real-world data problems with R and machine learning

Key Features:

• Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 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 experienced machine learning teacher and practitioner, 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, Third Edition provides a hands-on, 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 uncover key insights, make new predictions, and visualize your findings.

This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.

What You Will Learn:

• Discover the origins of machine learning and how exactly a computer learns by example
• Prepare your data for machine learning work with the R programming language
• 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 - the basis of deep learning
• Avoid bias in machine learning models
• Evaluate your models and improve their performance
• Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow

Who this book is for:

Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

R gives you access to the cutting-edge software you need to prepare data for machine learning. No previous knowledge required 'Äì this book will take you methodically through every stage of applying machine learning.

Key Features:

• Harness the power of R for statistical computing and data science
• Use R to apply common machine learning algorithms with real-world applications
• Prepare, examine, and visualize data for analysis
• Understand how to choose between machine learning models
• Packed with clear instructions to explore, forecast, and classify data

Book Description:

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of big data and data science. Given the growing prominence of R'Äîa cross-platform, zero-cost statistical programming environment'Äîthere has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.

Machine Learning with R is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions.

How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.

We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

Machine Learning with R will provide you with the analytical tools you need to quickly gain insight from complex data.

What You Will Learn:

• Understand the basic terminology of machine learning and how to differentiate among various machine learning approaches
• Use R to prepare data for machine learning
• Explore and visualize data with R
• Classify data using nearest neighbor methods
• Learn about Bayesian methods for classifying data
• Predict values using decision trees, rules, and support vector machines
• Forecast numeric values using linear regression
• Model data using neural networks
• Find patterns in data using association rules for market basket analysis
• Group data into clusters for segmentation
• Evaluate and improve the performance of machine learning models
• Learn specialized machine learning techniques for text mining, social network data, and 'Äúbig'Äù data

Who this book is for:

Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.