Mathematical Foundations For Machine Learning and AI
 
Artificial Intelligence (AI) has surged in significance in the past decade, profoundly impacting various facets of our daily lives. From self-driving cars to medical diagnostics and even defeating humans in strategy games like Go and Chess, the strides AI has taken are remarkable.

The future of AI holds promise, with the prospect of robotic companions not far off. This has spurred many developers to explore coding and developing AI and Machine Learning (ML) programs. However, mastering the art of writing algorithms for AI and ML demands extensive programming and mathematical proficiency.

Mathematics plays a pivotal role in laying the groundwork for programming in these domains. In this course, we've meticulously curated a comprehensive curriculum to help you master the mathematical foundations for crafting AI and ML programs and algorithms.

Developed in collaboration with industry experts, this course demystifies complex mathematical concepts, breaking them into more digestible ideas. It covers three main mathematical theories: Linear Algebra, Multivariate Calculus, and Probability Theory.

What you'll learn:

- Refresh your understanding of mathematical concepts pertinent to AI and Machine Learning.
- Learn to implement algorithms using Python.
- Grasp how these concepts translate to real-world ML problems.
- Explore topics including Scalars, Vectors, Matrices, Tensors, Matrix Norms, Eigenvalues, Eigenvectors, Elements of Probability, Random Variables, Distributions, Variance, Expectation, and Special Random Variables.

Who this course is for:

- Anyone seeking to refresh or acquire the mathematical tools necessary for AI and Machine Learning will find this course invaluable.

Course Components:

- Mathematical Foundation For Machine Learning and AI Course