Download Machine Learning with R 4th Edition
Machine Learning with R – Fourth Edition (4th Edition): Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data. 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.
- 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
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
eBook details
- Author (s): Brett Lantz
- Year of publication: 2023
- Publisher: Packt Publishing
- Language: English
- ISBN: 1801071322, 9781801071321
- Pages: 1162 pages
- Book format: PDF
- File size: 12 MB
