Your cart is empty.
Keep shoppingMathematics For Machine Learning
48 students
Mathematics For Machine Learning
The math you will need for your machine learning journey.
Created byQS Academy
Last updated 3/2025
English
English [Auto],
What you'll learn
- People who want to learn the mathematics that drives machine learning models.
- Students who are not sure about data science as a career and want to give it a serious try without paying college level tuition
- Data scientists who want a refresher in mathematics.
- Students who want to have a solid foundation in mathematics to proceed to more advanced machine learning models.
- Product managers who want to know how data scientists and machine learning engineers think.
- Machine Learning Engineers, who know how to deploy models, but want to know what is actually going underneath the hood of these models.
Explore related topics
Course content
6 sections • 40 lectures • 9h 3m total length
Requirements
- No programming or math experience necessary, foundational concepts are developed from scratch.
Description
This course provides a comprehensive foundation in the mathematical concepts essential for understanding and implementing machine learning algorithms from first principles. Students will explore Linear Algebra, covering vectors, matrices, eigenvalues, and singular value decomposition—critical for data representation and transformations. Multivariable Calculus will focus on gradients, Jacobians, and Hessians, which are fundamental to optimization techniques used in training models.
The course also introduces Probability and Statistics, covering key topics such as random variables, probability distributions, expectation, variance, and fundamental statistical inference techniques. Optimization methods, including gradient descent and related algorithms, will be explored to understand how machine learning models learn from data. Additionally, students will develop problem-solving skills by working through mathematical proofs and derivations that underpin these techniques.
Throughout the course, students will gain hands-on experience with NumPy and SciPy, leveraging these powerful Python libraries to implement mathematical concepts programmatically. Rather than applying models to real-world datasets, the focus will be on understanding and building the mathematical foundations necessary for machine learning. By the end of the course, students will have the necessary mathematical and computational tools to derive and implement machine learning techniques from scratch, preparing them for deeper study in artificial intelligence and data science, as well as advanced mathematical modeling.
Who this course is for:
- Anybody who wants to understand the mathematics behind machine learning models.
- Students, who are not sure if data science is a viable career for them.