Download InformIT – Linear Algebra for Machine Learning 2020-12

Description

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

What you will learn in the Linear Algebra for Machine Learning course:

Getting to know the application of algebra and the principles of mathematics in the field of machine learning
Getting to know the basic principles of linear algebra
Familiarity with different approaches to developing solutions based on machine learning
Deep understanding of the working process of machine learning based algorithms
Improve mathematical intuition skills
Deep understanding of other topics related to machine learning such as differential and integral calculus, statistics, optimization algorithms, etc.

Course details

Publisher: InformIT
teacher: Jon Krohn
English language
Education level: Intermediate
Number of courses: 58
Training duration: 6 hours and 32 minutes

Course headings

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 course 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.