By: MIT xPRO on November 18th, 2020
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Prepare for MIT xPRO’s Machine Learning Certificate with These 5 Free Online Learning Resources

Professional Development | Machine Learning

Can you take an online machine learning course without a background in data science or engineering? That’s the big question that MIT xPRO’s support team gets from professionals who are interested in earning an online certificate in Machine Learning, Modeling, and Simulation, but don’t feel prepared for the two-course online program.

Taught by MIT faculty at the MIT Center for Computational Science and Engineering (CCSE), this program connects science and engineering skills to the principles of machine learning and data science while bringing a hands-on approach to modern engineering problem-solving. 

There are no prerequisites to earn an MIT xPRO program certificate in machine learning, but a little preparation could help you get the most out of your coursework.

MIT OpenCourseWare (OCW) courses are self-paced and available for you to review at any time, but do not offer live support such as discussion forums or support from TAs. Reviewing these 5 free resources will help you:

  1. Strengthen your single variable calculus skills

This calculus course covers differentiation and integration of functions of one variable, and concludes with a brief discussion of infinite series. Calculus is fundamental to many scientific disciplines including physics, engineering, and economics.


  1. Strengthen your multivariable calculus skills

This course covers differential, integral and vector calculus for functions of more than one variable. These mathematical tools and methods are used extensively in the physical sciences, engineering, economics and computer graphics.


  1. Brush up on linear algebra

This MIT OpenCourseware course picks out four key applications in MIT Professor Strang’s textbook Introduction to Linear Algebra:

    • Graphs and Networks
    • Systems of Differential Equations
    • Least Squares and Projections
    • Fourier Series and the Fast Fourier Transform.
  1. Gain a foundation in statistics and probability

This course provides an elementary introduction to probability and statistics with applications. Topics include:

    • Basic combinatorics
    • Random variables
    • Probability distributions,
    • Bayesian inference
    • Hypothesis testing
    • Confidence intervals
    • Linear regression.


  1. Learn applicable statistical methods

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.


The time to solve problems with machine learning is now. Preparing with the online learning resources above will help you become more comfortable with the computational engineering principles and applications in this two-course online Machine Learning program from MIT.