ML Algorithms: Multivariate Calculation & Algorithms
Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level
Overview/Description
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 Taylor's theorem and the conditions for local minimum.
Expected Duration (hours)
0.6
Lesson Objectives
ML Algorithms: Multivariate Calculation & Algorithms
it_mlmdsndj_01_enus
Expertise Level
Intermediate