ML Algorithms: Multivariate Calculation & Algorithms

Learners can explore the role of multivariate calculus in machine learning (ML) and how to apply math to data science ML and deep learning in this 10-video course examining several ML algorithms and showing how to identify different types of variables. First learners will observe how to implement multivariate calculus derive function representations of calculus and utilize differentiation and linear algebra to optimize ML algorithms. Next you will examine how to use advanced calculus and discrete optimization to implement robust and high-performance ML applications. Then you will learn to use R and Python to implement multivariate calculus for ML and data science. You will learn about partial differentiation and its application on vector calculus and differential geometry and the use of product rule and chain rule. You will examine the role of linear algebra in ML and learn to classify the techniques of optimization by using gradient and Jacobian matrix. Finally you will explore Taylors theorem and the conditions for local minimum.<