This course is designed to give you a complete mathematical foundation for understanding and applying machine learning and AI methods in practice. Whether you’re preparing for advanced ML courses or working on real-world projects, this course ensures you understand the math that powers it all — without needing a degree in mathematics.
We start with linear algebra — vectors, subspaces, eigenvalues, and orthogonality — and explain how they’re used in ML algorithms. You’ll not only learn the theory but also see hands-on applications in Python and MATLAB, especially for eigenvalues and orthogonalization.
Then we move into calculus and multivariable calculus: functions, derivatives, partial derivatives, Hessians, and Lagrange multipliers. These are essential for understanding how models learn and optimize.
Next comes probability and statistics: foundational probability concepts, distributions, Bayes’ theorem, hypothesis testing, and MLE — everything you need for probabilistic reasoning in AI systems.
Finally, we go into optimization: gradient descent, RMSProp, AdaGrad, KKT conditions, and linear programming. You’ll understand how models are trained, constrained, and improved over time.
The content is clear, structured, and applied. You’ll learn both the mathematical logic and how it’s implemented in code. No prior programming or ML knowledge is assumed — just a desire to learn.
By the end of this course, you’ll be confident in your math skills and ready to move forward in your machine learning and AI journey.
- Aspiring data scientists, machine learning engineers, or AI enthusiasts who want a solid foundation in math.
- Students in engineering, computer science, or related fields preparing for advanced ML courses.
- Professionals looking to refresh or deepen their understanding of core math concepts used in AI.
