Machine Learning with R

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

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.

Table of Contents
Introducing Machine Learning
Managing and Understanding Data
Lazy Learning – Classification Using Nearest Neighbors
Probabilistic Learning – Classification Using Naive Bayes
Divide and Conquer – Classification Using Decision Trees and Rules
Forecasting Numeric Data – Regression Methods
Black-Box Methods – Neural Networks and Support Vector Machines
Finding Patterns – Market Basket Analysis Using Association Rules
Finding Groups of Data – Clustering with k-means
Evaluating Model Performance
Being Successful with Machine Learning
(N.B. Please use the Look Inside option to see further chapters)