Practical Machine Learning (Johns Hopkins University)

Practical Machine Learning is a self-paced online course offered by Johns Hopkins University via Coursera that introduces core concepts and methods used for building prediction functions. The course covers training and test sets, overfitting and error rates, and a range of model-based and algorithmic methods including regression, classification trees, Naive Bayes, and random forests.

It also walks through the end-to-end process of prediction: data collection, feature creation, algorithm selection, and evaluation. The course is structured into four modules with short videos, readings, assignments and peer reviews (approximately 8 hours total).

Learners complete practical assignments using tools introduced during the modules and can earn a shareable certificate by purchasing the Certificate experience. The course is taught in English and includes assessments and a course project.

Enrollment options include auditing the course for free (no certificate), purchasing the certificate experience, applying for financial aid, or accessing the course via Coursera Plus where available. The course is aimed at learners seeking foundational, job-relevant machine learning skills and is appropriate for those with some background in statistics or programming who want practical, hands-on exposure.