Machine Learning with R
Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
4th Edition
Published on 21. May 2024
762 pages
978-1-80107-605-0 (ISBN)
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Brett Lantz
Machine Learning with R
Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data
Book
05/2023
4th Edition
Packt Publishing
€59.20
Shipment within 3-4 weeks
Previous edition
E-Book
06/2024
3rd Edition
Packt Publishing Limited
€36.99
Available for download
Person
Lantz Brett :
Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.
Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.
Content
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
- Advanced Data Preparation
- Challenging Data - Too Much, Too Little, Too Complex
- Building Better Learners
- Making Use of Big Data
- 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
- Advanced Data Preparation
- Challenging Data - Too Much, Too Little, Too Complex
- Building Better Learners
- Making Use of Big Data
