# Download Math 0-1: Matrix Calculus in Data Science & Machine Learning

## Description

Math 0-1: Matrix Calculus in Data Science & Machine Learning, the training course for matrix calculus in data science and machine learning is published by Udemy Academy. Welcome to the exciting world of matrix calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will discuss the powerful mathematics that are the basis of many algorithms and techniques used in these fields. At the end of this course, you will have the necessary knowledge and skills to navigate the complex landscape of derivatives, gradients and matrix optimizations. Matrix calculus is the language of machine learning and data science. In these contexts we often work with high-dimensional data and transform matrices and their derivatives as a natural representation of our problems. Understanding matrix calculus is critical for developing and analyzing algorithms, building predictive models, and understanding large amounts of data. In the first part of this course, we will examine the principles of linear and quadratic forms and their derivatives. Linearity appears in the most basic and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We also address quadratic forms to understand optimization problems that appear in regression, portfolio optimization in finance, signal processing, and control theory.

### What you will learn

• Derive matrix and vector derivatives for linear and quadratic forms
• Solving common optimization problems (least square, Gaussian, portfolio)
• Understanding and implementing gradient descent and Newton’s method
• Teaching how to use the matrix book

### Who is this course suitable for?

• Students and professionals interested in the mathematics behind artificial intelligence, data science and machine learning

### Course specifications Math 0-1: Matrix Calculus in Data Science & Machine Learning

• Publisher: Udemy
• teacher : Lazy Programmer Inc.
• English language
• Education level: all levels
• Number of courses: 29
• Training duration: 4 hours and 33 minutes

### Course prerequisites

• Competence with Calculus and Linear Algebra
• Optional: Familiarity with Python, Numpy, and Matplotlib to implement optimization techniques

### Installation guide

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