<p>In this course, we will examine in detail the R software, which is the most popular statistical programming language of recent years.</p> <p>You will start with exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. From there, you will dive into the general structure of the clustering algorithms and develop applications in the R environment by using clustering and classification algorithms for real-life problems Next, you will learn to use general definitions about artificial neural networks, and the concept of deep learning will be introduced. The elements of deep learning neural networks, types of deep learning networks, frameworks used for deep learning applications will be addressed and applications will be done with R TensorFlow package. Finally, you will dive into developing machine learning applications with SparkR, and learn to make distributed jobs on SparkR.</p> <h1>Style and Approach</h1> <p>A step-by-step real world guide on machine learning and deep learning that takes you through the core aspects for building powerful data science applications with the help of the R programming language</p>