Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
You’re not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization; and many would-be AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a hands-on approach to working with data and applying the techniques you’ve learned.
This course is not a full math curriculum. It’s not designed to replace school or college math education. Instead, it focuses on the key mathematical concepts that you’ll encounter in studies of machine learning. It is designed to fill the gaps for students who missed these key concepts as part of their formal education, or who need to refresh their memories after a long break from studying math.
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.
Equations, Functions, and Graphs
Differentiation and Optimization
Vectors and Matrices
Statistics and Probability
Note: This syllabus is preliminary and subject to change.
Achinthcompleted this course, spending 2 hours a week on it and found the course difficulty to be medium.
Video tutorials are very short and does not go in-depth. The labs which are Jupyter Notebook files cover each module in-depth. Would have preferred if the video tutorials were longer and went in depth. Most of the assessment questions are pretty easy to solve.