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 Academy. In this training course, you will get acquainted with the theoretical and practical issues of linear algebra and you will 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 and…

Course specifications​

Publisher: InformIT
Instructor: Jon Krohn
Language: English
Level: Medium
Courses: 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

Prerequisites for Linear Algebra for Machine Learning​

Mathematics: Familiarity with secondary school-level mathematics will make the course easier to follow. If you are comfortable dealing with...

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