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.
