What you will learn
- Equations, Functions, and Graphs
- Differentiation and Optimization
- Vectors and Matrices
- Statistics and Probability
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.
Before you start
- A basic knowledge of math
- Some programming experience – Python is preferred.
- A willingness to learn through self-paced study.
- Instructor-Led: course contains assignments and exams that have specific due dates, and you complete the course within a defined time period.
- Course ends: Jan 22, 2019
Meet Your Instructors
Frequently Asked Questions
A: This course is specifically aimed at students who want to apply math to machine learning and artificial intelligence – Python is used extensively in these fields. The practical elements of this course involve implementing mathematical techniques in Python code. For the most part, you’ll be given the code you need to complete the exercises; but a basic knowledge of Python syntax will improve your understanding of what’s going on in the labs and demonstrations. Consider taking course DAT208x: Introduction to Python for Data Science before taking this class.
Q: What hardware and software do I need to complete this class?
A: You will need a computer running Windows, Mac OSX, or Linux and a web browser. Optionally, you can install Python 3.x – but you will be able to complete the labs using a free online environment, so this is not required.
Q: Will I need a Microsoft Azure subscription to complete this class?
A: No. You will be able to complete the labs using a local Python installation or the Microsoft Azure Notebooks service, which is a free service that does not require an Azure subscription.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.