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Coursera - Linear Algebra from Elementary to Advanced Specialization - OneDDL - 12-04-2023
![[Image: 672b1d9a6f3868e238fc2a3b541ef39f.jpeg]](https://i122.fastpic.org/big/2023/1204/9f/672b1d9a6f3868e238fc2a3b541ef39f.jpeg)
Free Download Coursera - Linear Algebra from Elementary to Advanced Specialization
Released 11/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 27 Lessons ( 9h 58m ) | Size: 735 MB
Learn Linear Algebra - the Theory of Everything!. Master techniques and theory of linear algebra
Skills you'll gain
Matrix Analysis
This specialization is a three course sequence that will cover the main topics of undergraduate linear algebra. Defined simply, linear algebra is a branch of mathematics that studies vectors, matrices, lines and the areas and spaces they create. These concepts are foundational to almost every industry and discipline, giving linear algebra the informal name "The Theory of Everything".
This specialization assumes no prior knowledge of linear algebra and requires no calculus or similar courses as a prerequisite. The first course starts with the study of linear equations and matrices. Matrices and their properties, such as the determinant and eigenvalues are covered. The specialization ends with the theory of symmetric matrices and quadratic forms. Theory, applications, and examples are presented throughout the course. Examples and pictures are provided in low dimensions before abstracting to higher dimensions. An equal emphasis is placed on both algebraic manipulation as well as geometric understanding of the concepts of linear algebra.
Upon completion of this specialization , students will be prepared for advanced topics in data science, AI, machine learning, finance, mathematics, computer science, or economics.
Applied Learning Project
Learners will have the opportunity to complete special projects in the course. Projects include exploration of advanced topics in mathematics and their relevant applications. Project topics include Markov Chains, the Google PageRank matrix, and recursion removal using eigenvalues.
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