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: InformITInstructor: Jon Krohn
Language: English
Level: Medium
Courses: 58
Duration: 6 hours and 32 minutes
Course topics
Lesson 1: Orientation to Linear AlgebraLesson 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...Read more
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