Essential Math for Machine Learning: Python Edition機器學習數學精要-Python版

  • 開課機構: 財團法人資訊工業策進會-微軟專業學程MPP
  • 開課平台: 資策會-數位學習平台
  • 講師介紹: Graeme Malcolm

  • Graeme Malcolm

    Senior Content Developer

    Microsoft Learning Experiences

    Graeme has been a trainer, consultant, and author for longer than he cares to remember, specializing in SQL Server and the Microsoft data platform. He is a Microsoft Certified Solutions Expert for the SQL Server Data Platform and Business Intelligence. After years of working with Microsoft as a partner and vendor, he now works in the Microsoft Learning Experiences team as a senior content developer, where he plans and creates content for developers and data professionals who want to get the best out of Microsoft technologies.

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    About this course

    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.


    What you'll learn

    After completing this course, you will be familiar with the following mathematical concepts and techniques:
    •Equations, Functions, and Graphs
    •Differentiation and Optimization
    •Vectors and Matrices
    •Statistics and Probability



    Prerequisites: •A basic knowledge of math •Some programming experience - Python is preferred.•A willingness to learn through self-paced study