Machine Learning with R: Expert techniques for predictive modeling,3rd Edition
Authors: Brett Lantz
ISBN-10: 1788295862
ISBN-13: 9781788295864
Publisher finelybook 出版社: Packt Publishing (April 15,2019)
Print Length 页数: 458 pages
Book Description
By finelybook
Solve real-world data problems with R and machine learning
Machine learning,at its core,is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
Machine Learning with R,Third Edition provides a hands-on,readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language,Brett Lantz teaches you everything you need to uncover key insights,make new predictions,and visualize your findings.
This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries,advice on ethical and bias issues in machine learning,and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
What you will learn
Discover the origins of machine learning and how exactly a computer learns by example
Prepare your data for machine learning work with the R programming language
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 ― the basis of deep learning
Avoid bias in machine learning models
Evaluate your models and improve their performance
Connect R to SQL databases and emerging big data technologies such as Spark,H2O,and TensorFlow
contents
1 Introducing Machine Learning
2 Managing and Understanding Data
3 Lazy Learning – Classification Using Nearest Neighbors
4 Probabilistic Learning – Classification Using Naive Bayes
5 Divide and Conquer – Classification Using Decision Trees and Rules
6 Forecasting Numeric Data – Regression Methods
7 Black Box Methods – Neural Networks and Support Vector Machines
8 Finding Patterns – Market Basket Analysis Using Association Rules
9 Finding Groups of Data – Clustering with k-means
10 Evaluating Model Performance
11 Improving Model Performance
12 Specialized Machine Learning Topics
