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 LearningMathematics: 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.