Book description
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.
Table of contents
- Preface
- Introducing Machine Learning
-
Managing and Understanding Data
- R data structures
- Managing data with R
-
Exploring and understanding data
- Exploring the structure of data
-
Exploring numeric features
- Measuring the central tendency – mean and median
- Measuring spread – quartiles and the five-number summary
- Visualizing numeric features – boxplots
- Visualizing numeric features – histograms
- Understanding numeric data – uniform and normal distributions
- Measuring spread – variance and standard deviation
- Exploring categorical features
- Exploring relationships between features
- Summary
- Lazy Learning – Classification Using Nearest Neighbors
- Probabilistic Learning – Classification Using Naive Bayes
-
Divide and Conquer – Classification Using Decision Trees and Rules
- Understanding decision trees
- Example – identifying risky bank loans using C5.0 decision trees
- Understanding classification rules
- Example – identifying poisonous mushrooms with rule learners
- Summary
-
Forecasting Numeric Data – Regression Methods
- Understanding regression
- Example – predicting auto insurance claims costs using linear regression
- Understanding regression trees and model trees
- Example – estimating the quality of wines with regression trees and model trees
- Summary
- 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
- Measuring performance for classification
- Estimating future performance
- Summary
- Being Successful with Machine Learning
- Advanced Data Preparation
- Challenging Data – Too Much, Too Little, Too Complex
- Building Better Learners
-
Making Use of Big Data
- Practical applications of deep learning
- Unsupervised learning and big data
- Adapting R to handle large datasets
- Summary
- Other Books You May Enjoy
- Index
Product information
- Title: Machine Learning with R - Fourth Edition
- Author(s):
- Release date: May 2023
- Publisher(s): Packt Publishing
- ISBN: 9781801071321
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