InformIT – Linear Algebra for Machine Learning 2020-12

Published on: 2024-09-27 17:28:31

Categories: 28

Description

Linear Algebra for Machine Learning is a training course on the application of linear algebra in data science and machine learning, published by InformIT Academy. In this training course, get acquainted with the theoretical and practical issues of linear algebra and implement it in a completely practical way in projects related to machine learning. Machine learning and data science are two of the most widely used disciplines in today’s digital world, and learning them can bring you many career opportunities.

What you will learn in Linear Algebra for Machine Learning

Familiarity with the application of algebra and the principles of mathematics in the field of machine learning
Familiarity with the basics of linear algebra
Familiarity with different approaches to developing machine learning based solutions
In-depth understanding of the working process of machine learning-based algorithms
Improve the skills of mathematical intuition
In-depth understanding of other topics related to machine learning such as calculus, statistics, optimization algorithms, etc.

Course specifications

Publisher: InformIT
Instructors: Jon Krohn
Language: English
Level: Intermediate
Number of Lessons: 58
Duration: 6 hours and 32 minutes

Course topics

Lesson 1: Orientation to Linear Algebra

Lesson 2: Data Structures for Algebra

Lesson 3: Common Tensor Operations

Lesson 4: Solving Linear Systems

Lesson 5: Matrix Multiplication

Lesson 6: Special Matrices and Matrix Operations

Lesson 7: Eigenvectors and Eigenvalues

Lesson 8: Matrix Determinants and Decomposition

Lesson 9: Machine Learning with Linear Algebra

Linear Algebra for Machine Learning Prerequisites

Mathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with quantitative information — such as understanding charts and rearranging simple equations — then you should be well-prepared to follow along with all of the mathematics.

Programming: All code demos are in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.