In DataCamp’s brand new course, Introduction to Machine Learning with R, you’ll learn all about the most widely used machine learning techniques such as classification, regression and clustering. Whether you want to build your personal recommendation engine, become better at picking stocks, or develop your own self-driving car, machine learning is essential. It nicely interweaves statistics with computer science, and harnesses the predictive power of this synergy.Introduction to Machine Learning introduces you to the most effective machine learning techniques, and shows you how to implement these using R. The tutorial first dives into the assessment and training of different machine learning models, and switches gears in a later phase when focusing on machine learning tasks such as classification, regression and clustering. Since the best way to learn is by doing, you’ll gain practice implementing your own models thanks to our familiar interactive in-browser coding interface.Start the first chapter for free.
What you will learnOur Introduction to Machine Learning tutorial is a perfect fit if you are familiar with the basics of R and statistics, but completely new to machine learning. Start to finish, it will take you approximately 6 hours to complete. You can take this online tutorial at your own pace, and everything takes place in the comfort of your own browser. So no need to install packages, get your system up to date, or any other annoying tasks that keep you from learning.
- Chapter One: What is machine learningBefore implementing your own models, you first need to understand what machine learning is. In this first chapter you will do your first experimentation using some of the techniques that will be covered in more detail later on.
- Chapter Two: Performance measuresUnderstand how to assess the performance of supervised and unsupervised learning algorithms. Learn to split your data into a train and a test set, and discover the concepts of bias and variance.
- Chapter Three: ClassificationOne of the most important tasks in machine learning is classification. By the end of this chapter you will be able to build your own decision tree and classify unseen observations with k-Nearest Neighbors.
- Chapter Four: RegressionLearn more on the the predictive capabilities and performance of regression algorithms. Get acquainted with simple linear regression, multi-linear regression and k-Nearest Neighbors regression.
- Chapter Five: ClusteringIn the final chapter you will be introduced to an unsupervised learning technique: clustering. Discover the power of k-means clustering and hierarchical clustering.