Math 0-1 - Matrix Calculus in Data Science and Machine Learning.

Format:

Video

Language:

English

Physical Description:

1 online resource (377 minutes)

Place of Publication:

[Place of publication not identified] : PACKT Publishing, 2024.

Language Note:

In English.

System Details:

video file

Summary:

Dive into the essentials of matrix and vector derivatives for Data Science & ML. This course guides you from basics to optimization techniques, with practical Python applications and comprehensive learning strategies. Key Features: Comprehensive coverage of matrix calculus and its applications in machine learning. Detailed guidance on setting up a learning environment with essential tools and libraries. Tailored learning strategies to suit both beginners and advanced learners in the field. . course Description: This course starts with an introduction to the key concepts and outlines the roadmap to success in the field. You'll begin by understanding the foundational elements of matrix and vector derivatives, Exploreing topics like linear and quadratic forms, chain rules in matrix form, and the derivative of determinants. Each concept is reinforced with exercises, ranging from quadratic challenges to least squares and Gaussian methods. The course progresses into optimization techniques essential in data science and machine learning. Delve into multi-dimensional second derivative tests, gradient descent in one and multiple dimensions, and Newton's method, including practical exercises in Newton's Method for least squares. An additional focus is set on setting up your environment, where you'll learn to establish an Anaconda environment and install crucial tools like Numpy, Scipy, and TensorFlow. The course also addresses effective learning strategies, answering pivotal questions like the suitability of YouTube for learning calculus and the recommended order for taking courses in this field. As you journey through the course, you'll transition from foundational concepts to advanced applications, equipping yourself with the skills needed to excel in data science and machine learning. What you will learn: Understand matrix and vector derivatives. Master linear and quadratic forms. Apply the chain rule in matrix calculus. Solve optimization problems using gradient descent and Newton's method. Set up the Anaconda environment for machine learning. Install and use key libraries like Numpy and TensorFlow. Develop effective strategies for learning calculus in data science. Who this course is for: This course suits students and professionals eager to learn the math behind AI, Data Science, and Machine Learning, ideal for deepening knowledge in these advanced technology fields. Learners should have a basic knowledge of linear algebra, calculus, and Python programming to effectively understand matrix calculus. A keen interest and enthusiasm for Exploreing this intricate subject are also crucial for a fulfilling learning experience.

Notes:

Title from resource description page (viewed November 04, 2024).