The integration of Artificial Intelligence is growing and multiple sectors are now looking to build technologies that include AI. With self-driving cars, smart robots, to even your coffee machines, AI has become a prominent technology that cannot be overlooked.
Writing algorithms for AI and Machine Learning is difficult and requires extensive programming and mathematical knowledge. While these algorithms have the potential to solve a number of difficult problems that are currently plaguing the world, designing these algorithms to solve these problems requires intricate mathematical skills and experience.
In this course, we have tried to help you cover exactly that. The course delves deep into the world of mathematics and algorithms to help you get started understanding these complex concepts. The course will help you learn the mathematical background you need to start working on building algorithms and networks for your next machine learning and AI projects.
The course has been designed to help breakdown these mathematical concepts and ideas by dividing the syllabus into three main sections which include:
Linear Algebra - Throughout the field of Machine Learning, linear algebra notation is used to describe the parameters and structure of different machine learning algorithms. This makes linear algebra a necessity to understand how neural networks are put together and how they are operating.
Multivariate Calculus - This is used to supplement the learning part of machine learning. It is what is used to learn from examples, update the parameters of different models and improve the performance.
Probability Theory - The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms. It is important for us to understand the key probability distributions, and we will cover it in depth in this course.
What you will learn in this course:
- Scalars, Vectors, Matrices, Tensors
- Matrix Norms
- Special Matrices and Vectors
- Eigenvalues and Eigenvectors
- Differential Operators
- Convex Optimization
- Elements of Probability
- Random Variables
- Variance and Expectation
- Special Random Variables
Thats not all. The course also comes with projects and quizzes to help solidify your knowledge of the mathematical concepts. At the end of this course, you will have the mathematical foundation necessary to understand and analyze deep neural networks, the most common artificial intelligence algorithm.
So, what are you waiting for? Get your hands on one of the most comprehensive mathematical foundation course and start building your own AI and ML algorithms!