Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic.
Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering.
With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights.
Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques.
Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.
Learn the end-to-end process of machine learning from raw data to implementation
Classify important outcomes using nearest neighbor and Bayesian methods
Predict future events using decision trees, rules, and support vector machines
Forecast numeric data and estimate financial values using regression methods
Model complex processes with artificial neural networks
Prepare, transform, and clean data using the tidyverse
Evaluate your models and improve their performance
Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow