About the Book: Machine Learning With R: Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight into Real-World Applications Machine learning, at its core, is concerned with transforming data into actionable knowledge.
ThisMoreAbout the Book: Machine Learning With R: Learn How to Use R to Apply Powerful Machine Learning Methods and Gain an Insight into Real-World Applications Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of big data and data science. Given the growing prominence of R-a cross-platform, zero-cost statistical programming environment-there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data.
Machine Learning with R is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process.
We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. Machine Learning with R will provide you with the analytical tools you need to quickly gain insight from complex data.
Content Preface Chapter 1: Introducing Machine Learning Chapter 2: Managing and Understanding Data Chapter 3: Lazy Learning-Classification Using Nearest Neighbors Chapter 4: Probabilistic Learning-Classification Using Naive Bayes Chapter 5: Divide and Conquer-Class