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edX Essential Math for Machine Learning: R Edition

Microsoft via edX

  • Overview
  1. edX
    Platform:
    edX
    Provider:
    Microsoft
    Length:
    6 weeks
    Effort:
    6 to 8 hours per week
    Language:
    English
    Credentials:
    Paid Certificate Available
    Part of:
    Microsoft Professional Program Certificate in Data Science
    Overview
    This course is part of the Microsoft Professional Program Certificate in Data Science.

    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.

    What You Will Learn
    • Familiarity with Equations, Functions, and Graphs
    • Differentiation and Optimization
    • Vectors and Matrices
    • Statistics and Probability
    Syllabus
    • Introduction
    • Equations, Functions, and Graphs
    • Differentiation and Optimization
    • Vectors and Matrices
    • Statistics and Probability
    Note: This syllabus is preliminary and subject to change.

    Taught by
    Graeme Malcolm

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