Course Description

This course is designed for those interested in understanding machine learning and AI concepts through the lens of mathematics without becoming a professional mathematician. It focuses on teaching essential mathematical concepts, such as Algebra, Calculus, and Statistics, by applying them in Python. This practical approach not only eases the understanding of complex theories but also helps in applying them to real-world problems. This course is specially authored by Microsoft Corporation, ensuring up-to-date and industry-relevant content.

What Students Will Learn

  • How to interpret and solve equations, functions, and graphs
  • Methods of differentiation and optimization for calculating minimal or maximal values
  • Understanding of vectors and matrices which are foundational in data manipulation and transformation
  • Basic statistics and probability essential for data-driven decision-making

Prerequisites

Students wishing to enroll in this course should have:

  • A basic understanding of math fundamentals
  • Elementary proficiency with Python programming

Course Coverage

  • Mathematical Functions: Dive into algebraic concepts that form the backbone of many machine learning algorithms.
  • Calculus and Optimization: Learn how to find derivatives and use them to solve real-life problems involving optimization.
  • Vector Algebra and Matrix Operations: Understand linear transformations essential in deep learning and neural networks.
  • Statistics: Get a handle on data distributions, variances, and how probability affects model accuracy and predictions.

Who This Course is For

This course is ideal for:

  • Students and professionals eager to deepen their understanding of machine learning and artificial intelligence.
  • Individuals wanting to apply mathematical concepts practically using Python.
  • Learners who have basic math and programming knowledge and want to venture into AI and data science.

Application of Skills in the Real World

The skills learned in this course can be directly applied in various ways, such as:

  • Data Analysis: Use statistical and algebraic skills to interpret and analyze data more effectively.
  • Machine Learning Models: Implement machine learning algorithms by understanding the underlying mathematical computations.
  • Problem Solving in Tech: Apply calculus and optimization to solve resource allocation and cost minimization problems in tech implementations.