فهرست مطالب

• Machine Learning with R
• Cover
• Copyright
• Contributors
• Table of Contents
• Preface
• Chapter 1: Introducing Machine Learning
• The origins of machine learning
• Uses and abuses of machine learning
• How machines learn
• Machine learning in practice
• Machine learning with R
• Summary
• Chapter 2: Managing and Understanding Data
• R data structures
• Managing data with R
• Exploring and understanding data
• Summary
• Chapter 3: Lazy Learning – Classification Using Nearest Neighbors
• Understanding nearest neighbor classification
• Example – diagnosing breast cancer with the k-NN algorithm
• Summary
• Chapter 4: Probabilistic Learning – Classification Using Naive Bayes
• Understanding Naive Bayes
• Example – filtering mobile phone spam with the Naive Bayes algorithm
• Summary
• Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules
• Understanding decision trees
• Example – identifying risky bank loans using C5.0 decision trees
• Understanding classification rules
• Example – identifying poisonous mushrooms with rule learners
• Summary
• Chapter 6: Forecasting Numeric Data – Regression Methods
• Understanding regression
• Example – predicting auto insurance claims costs using linear regression
• Understanding regression trees and model trees
• Example – estimating the quality of wines with regression trees and model trees
• Summary
• Chapter 7: Black-Box Methods – Neural Networks and Support Vector Machines
• Understanding neural networks
• Example – modeling the strength of concrete with ANNs
• Understanding support vector machines
• Example – performing OCR with SVMs
• Summary
• Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules
• Understanding association rules
• Example – identifying frequently purchased groceries with association rules
• Summary
• Chapter 9: Finding Groups of Data – Clustering with k-means
• Understanding clustering
• Finding teen market segments using k-means clustering
• Summary
• Chapter 10: Evaluating Model Performance
• Measuring performance for classification
• Estimating future performance
• Summary
• Chapter 11: Being Successful with Machine Learning
• What makes a successful machine learning practitioner?
• What makes a successful machine learning model?
• Putting the “science” in data science
• Summary
• Chapter 12: Advanced Data Preparation
• Performing feature engineering
• Feature engineering in practice
• Exploring R’s tidyverse
• Summary
• Chapter 13: Challenging Data – Too Much, Too Little, Too Complex
• The challenge of high-dimension data
• Making use of sparse data
• Handling missing data
• The problem of imbalanced data
• Summary
• Chapter 14: Building Better Learners
• Tuning stock models for better performance
• Improving model performance with ensembles
• Stacking models for meta-learning
• Summary
• Chapter 15: Making Use of Big Data
• Practical applications of deep learning
• Unsupervised learning and big data
• Adapting R to handle large datasets
• Summary
• Other Books You May Enjoy
• Index