A note on book covers: while we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.
Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R. This book covers harnessing R for statistical computing and data science, exploring, forecasting, and classifying data. It is suitable for those with some knowledge of machine learning or R, providing a quick start. The book includes practical applications of algorithms such as Bayesian, nearest neighbor, decision trees, support vector machines, linear regression, and neural networks, with insights into text mining, social networks, and big data analysis.
A note on book covers: while we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.
Practical guide to machine learning with R for data enthusiasts.
Who is this book for?
If you're looking to get hands-on with machine learning using R, this book is a great pick. It covers a wide array of algorithms and provides practical applications, making complex techniques approachable for those with some background knowledge. You'll appreciate the clear explanations and real-world insights, perfect for anyone eager to deepen their data analysis skills.
