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, part of Harvard University's Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data, and how to use a set of data to discover potentially predictive relationships. 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.
Topics of study
The basics of machine learning
How to perform cross-validation to avoid overtraining
Several popular machine learning algorithms
How to build a recommendation system
What is regularization and why it is useful?
About Harvard University
Harvard University is devoted to excellence in teaching, learning and research, and to developing leaders in many disciplines who make a difference globally. Harvard faculty are engaged with teaching and research to push the boundaries of human knowledge. The University has 12 degree-granting schools in addition to the Radcliffe Institute for Advanced Study.
Established in 1636, Harvard is the oldest institution of higher education in the United States. The University, which is based in Cambridge and Boston, Massachusetts, has an enrollment of over 20,000 degree candidates, including undergraduate, graduate and professional students. Harvard has more than 360,000 alumni around the world.