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
Publisher:Packt Publishing Limited
By: Brett Lantz
Paid access
|May 2024
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
PDF ISBN: 978-1-80107-605-0
Publisher: Packt Publishing Limited
Copyright owner: © 2023 Packt Publishing Limited
Publication date: 2024
Language: English
Pages: 762
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