Practical Machine Learning

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science Specialization.

About The Course

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Frequently Asked Questions

How do the courses in the Data Science Specialization depend on each other?
We have created a handy course dependency chart to help you see how the nine courses in the specialization depend on each other.

Will I get a Statement of Accomplishment after completing this class?
Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?
Students must have an active GitHub account and the latest version of R and RStudio installed.

How does this course fit into the Data Science Specialization?
This is the eighth course in the sequence. Although it isn't a requirement, we recommend that you first take The Data Scientist’s Toolbox, R Programming, Regression Models, and Exploratory Data Analysis.

Recommended Background

The Data Scientist’s Toolbox, R Programming, Regression Models, and Exploratory Data Analysis