Practical Machine Learning in R is a book that introduces machine learning concepts and algorithms using the R programming language. Below is a summary of the concepts I learned from each chapter and the code for the book exercises that I completed.
Please click on the blue links below for the GitHub code of the book’s examples and the end of chapter exercises.
CHAPTER 3: Managing Data
This chapter focuses on how to prepare data prior to modeling using the tidyverse package.
CHAPTER 4: Linear Regression
This chapter focuses on how to build, evaluate, interpret, improve upon, and apply the results of a multiple linear regression model using R.
CHAPTER 5: Logistic Regression
This chapter focuses on how to build, evaluate, interpret, improve upon, and apply the results of a logistic regression model using R.
CHAPTER 6: k-Nearest Neighbors
This chapter focuses on how to use the k-NN classifier to assign labels to new data in R.
CHAPTER 7: Naive Bayes
This chapter focuses on how to build a naive Bayes classifier in R and how to use it to predict the class values of previously unseen data.
CHAPTER 8: Decision Trees
This chapter focuses on how to build a decision tree classifier in R and how to use it to predict the class values of previously unseen data.
CHAPTER 9: Evaluating Performance
This chapter focuses on how to evaluate model performance with metrics other than accuracy.
CHAPTER 10: Improving Performance
This chapter focuses on how to improve performance by tuning the parameters of a model to make it better and how to improve performance by combining several weak models together to achieve better results.
CHAPTER 11: Discovering Patterns with Association Rules
This chapter focuses on different ways to evaluate and quantify the strength of association rules.
CHAPTER 12: Grouping Data with Clustering
This chapter focuses on how to segment data using the k-means algorithm in R.
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