Mathematics for Machine Learning, Data Science and GenAI

Short Summary about the need and importance of the Course Linear Algebra is the backbone of Data Science, Machine Learning (ML), and Artificial Intelligence (AI). Understanding its core concepts is essential to grasp the functionality of ML algorithms. However, most courses make this process overwhelming by focusing on complex calculations rather than the practical application you need to understand the working of Machine Learning Algorithms. How our course is different ?We’ve designed this Linear Algebra course specifically for aspiring Data Scientists and Machine Learning enthusiasts who want to dive into the essentials without wasting time. In just around 7.5 hours, you’ll master the key concepts required for Machine Learning, with a clear focus on how these concepts apply directly to real-world Machine Learning algorithms. This Course will teach you the geometric intuition and essential computations so that you can think like a Machine Learning Expert.Please find the Complete Syllabus for the Course below Mathematics for Machine Learning: 1. Introduction to linear Algebra Difference between Algebra and Linear Algebra, Definition of Linear Algebra, Linear Equation and System of linear equations with an Example, Attributes and properties of system of linear equation.Mathematics for Machine Learning: 2. Geometric representation of an expression Geometric visualization of an algebraic expression with an example, Gradient of a straight line, Generalization of an expression geometrically on an N dimensional plane.Mathematics for Machine Learning: 3. Importance of a System of linear Equation Definition and Goal of System of Linear Equations, General form of system of Linear Equations, representing a dataset in terms of System of linear equations, Applications of system of linear equations in solving a classification and a regression problem with an e