Practical Machine Learning in R
Practical Machine Learning in R
About the Book
The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules.
The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki.
Part I - Introduction
Chapter 1 - Introduction to R
- 1.1 The basics
- 1.2 Vectors
- 1.3 Matrices
- 1.4 Data frames
- 1.5 R Scripts
- 1.6 Functions
1.8 Making decisions (
- 1.9 Datasets and statistics
- 1.10 Factors
- 1.11 Challenge
Chapter 2 - Introduction to Machine Learning
- 2.1 Definition
- 2.2 Main categories and tasks
- 2.3 Survival guide to machine learning
- Chapter 1 - Introduction to R
Part II - Classification
Chapter 3 - Classification with Decision trees
- 3.1 Introduction
- 3.2 Splitting Criteria and Decision Tree Construction
- 3.3 Application with Pruning and Evaluation Metrics
- 3.4 Exercise
Chapter 4 - Classification with Naive Bayes
- 4.1 Introduction
- 4.2 Naive Bayes Model Construction and Classification
- 4.3 Naive Bayes Application with Evaluation
Chapter 5 - Classification with k-Nearest Neighbors
- 5.1 Introduction
- 5.2 k-Nearest Neighbors Model Construction and Classification
- 5.3 k-Nearest Neighbors Real World Example
Chapter 6 - Classification with Support Vector Machines
- 6.1 Introduction
- 6.2 Support Vector Machines Model Construction and Classification
- 6.3 Exercise
- Chapter 3 - Classification with Decision trees
Part III - Data Processing
Chapter 7 - Feature Selection
- 7.1 Introduction
- 7.2 Filter Methods
- 7.2 Wrapper Methods
Chapter 8 - Dimensionality Reduction
- 8.1 Principal Components Analysis
- Chapter 7 - Feature Selection
Part III - Clustering
Chapter 9 - Centroid-based clustering and Evaluation
- 9.1 - k-Means in R
- 9.2 - k-Medoids in R
- 9.3 - Clustering Evaluation in R
- 9.4 - k-Means clustering overview
- 9.5 - k-Means clustering and evaluation in real life Application
- 9.6 - k-Medoids Application
Chapter 10 - Connectivity-based clustering
- 10.1 - Hierarchical clustering in R
- 10.2 - Hierarchical Clustering Application and Evaluation
Chapter 11 - Density-based clustering
- 11.1 - DBSCAN in R
- 11.2 - DBSCAN model construction
- 11.3 - DBSCAN calculation
- 11.4 - DBSCAN clustering with R
- 11.5 - Density-based Clustering Application
Chapter 12 - Distribution-based clustering
- 12.1 - Theoretical background
- 12.2 - Modeling Gaussian Mixture Models using EM
- 12.3 - GMMs Clustering Application
- 12.4 - GMMs Application with Information Criteria
- Chapter 9 - Centroid-based clustering and Evaluation
Part V - Extended Topics
- Chapter 13 - Association Rules
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