This is the eighth course in the HarvardX Professional Certificate in Data Science, a series of courses that prepare you to do data analysis in R, from simple computations to machine learning. We assume you have taken the previous seven courses in the series and are comfortable programming in R.
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
In this course, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, a set of data used to discover potentially predictive relationships, and how the data can come in the form of the outcome we want to predict and features that we will use to predict this outcome. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.
The bookdown-version of this course is available on this Github Page