Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications

✍ Scribed by Lantz, Brett

Publisher: PACKT PUBLISHING
Year: 2013
Tongue: English
Series: Open source community experience distilled
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.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.

✦ 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 --
Improving model performance --
Specialized machine learning topics.

✦ Subjects

Computer Science;Programming;Reference;Science;Nonfiction;Computers;Academic;Artificial Intelligence

📜 SIMILAR VOLUMES

An Introduction to Machine learning: wit

📁 An Introduction to Machine learning: with Application in R

✍ Clark M. 📂 Library 🌐 English

Center for Social Research Univercity of Notre Dame, 2013. – 42 p. – ISBN: N/A<div class="bb-sep"></div>The purpose of this document is to provide a conceptual introduction to statistical or machine learning (ML) techniques for those that might not normally be exposed to such approaches during their

R machine learning essentials gain quick

📁 R machine learning essentials gain quick access to the machine learning concepts and practical applications using the R development environment

✍ Usuelli, Michele 📂 Library 📅 2014 🏛 Packt Publishing 🌐 English

R machine learning essentials gain quick

📁 R machine learning essentials gain quick access to the machine learning concepts and practical applications using the R development environment

✍ Usuelli, Michele 📂 Library 📅 2014 🌐 English

Mastering Machine Learning with scikit-l

📁 Mastering Machine Learning with scikit-learn: Apply effective learning algorithms to real-world problems using scikit-learn

✍ Gavin Hackeling 📂 Library 📅 2017 🏛 Packt Publishing 🌐 English

Key Features • Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks • Learn how to build and evaluate performance of efficient models using scikit-learn • Practical guide to master your basi

Mastering machine learning with scikit-l

📁 Mastering machine learning with scikit-learn: apply effective learning algorithms to real-world problems using scikit-learn

✍ Hackeling, Gavin 📂 Library 📅 2014 🏛 Packt Publishing 🌐 English

Apply effective learning algorithms to real-world problems using scikit-learnAbout This Book Design and troubleshoot machine learning systems for common tasks including regression, classification, and clustering Acquaint yourself with popular machine learning algorithms, including decision trees, lo

Mastering Machine Learning with scikit-l

📁 Mastering Machine Learning with scikit-learn: Apply effective learning algorithms to real-world problems using scikit-learn

✍ Gavin Hackeling 📂 Library 📅 2014 🏛 Packt Publishing 🌐 English

This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-uns