Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.
In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, and Azure Notebooks.
edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.
Note: These courses will retire in June. Please enroll only if you are able to finish your coursework in time.
Introduction to Machine Learning
Data Preparation and Cleaning
Getting Started with Supervised Learning
Improving Model Performance
Machine Learning Algorithms
Note: This syllabus is preliminary and subject to change.
Graeme Malcolm, Steve Elston, Cynthia Rudin and Jonathan Sanito
Berbelek completed this course, spending 11 hours a week on it and found the course difficulty to be easy.
It took me about 11h to complete the course. I do not recommend it. The list of subjects is great, but the course does not offer interesting materials, the concepts are not properly explained and there is also no practice (a lot of pre-written code used by authors, no writing code expected by you). There are a lot of better intros to R available around the Intetnet.