Practical Machine Learning : In R

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 author gives 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.

Do you want to start using R for crunching machine learning models right from the start with examples? Then this book is for you.

Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language

Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms.

Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more.

Explores data management techniques, including data collection, exploration and dimensionality reduction
Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering
Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques
Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost
Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.