Linear Algebra Basics from Coursera

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Getting Started with the Course

This module provides an overview of the course content and structure. In this module, you will learn about the different course elements. In this module, you will get acquainted with your instructor and get an opportunity to introduce yourself and interact with your peers.

Vector Space

In this module, you will learn about vector space and its subspace. Further, you will learn about the set of linearly dependent and independent vectors. You will also gain insight into the linear combination and linear span of a set of vectors.

Linear Transformations and Eigenvalues

In this module, you will learn about the basis and dimension of a vector space. You will learn about the concept of linear transformations defined on real vector spaces. Further, you will understand that there is a matrix associated with each linear transformation for the bases. Finally, you will get an insight into the eigenvalues of a square matrix.

Diagonalizable Matrices and Their Applications

In this module, you will learn about the eigenvectors corresponding to the eigenvalues of a matrix. You will then learn about the properties of special matrices (symmetric and skew-symmetric). Finally, you will learn about the concept of diagonalization of a matrix (eigen decomposition of a matrix) with its applications.

Singular Value Decomposition of a Matrix and Inner Product of Vectors

In this module, you will learn about the spectral value decomposition and singular value decomposition of a matrix with some applications. Further, you will learn about the inner product space and norms of vectors and matrices with two useful identities—Cauchy-Schwarz inequality and Polarization identity—for machine learning algorithms.

Term-End Assignment

In this module, you are provided with your term-end project, instructions to complete the project, and the criteria for how your instructor will grade your submission.

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Taught by Dr. S. K. Gupta, who is recognized for their work in the field of machine learning and data science

Builds a strong foundation for beginners in linear algebra, which is a core skill for machine learning and data science

Covers the mathematical concepts related to linear algebra, which include vector spaces, subspaces, linear span, basis, and dimension

Career center

Learners who complete Linear Algebra Basics will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use linear algebra to solve complex problems in various industries, including finance, healthcare, and manufacturing. This course can help build a foundation in linear algebra, which is essential for understanding and applying machine learning algorithms. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in data science.

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Machine Learning Engineer

Machine Learning Engineers use linear algebra to develop and implement machine learning models. This course can help build a strong foundation in linear algebra, which is essential for understanding machine learning algorithms. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in machine learning.

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Quantitative Analyst

Quantitative Analysts use linear algebra to model and analyze financial data. This course can help build a strong foundation in linear algebra, which is essential for understanding and applying quantitative analysis techniques. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in quantitative analysis.

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Actuary

Actuaries use linear algebra to model and analyze insurance and financial data. This course can help build a strong foundation in linear algebra, which is essential for understanding and applying actuarial techniques. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in actuarial science.

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Financial Analyst

Financial Analysts use linear algebra to model and analyze financial data. This course can help build a strong foundation in linear algebra, which is essential for understanding and applying financial analysis techniques. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in financial analysis.

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Operations Research Analyst

Operations Research Analysts use linear algebra to optimize business processes. This course can help build a strong foundation in linear algebra, which is essential for understanding and applying operations research techniques. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in operations research.

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Software Engineer

Software Engineers use linear algebra to develop and implement software algorithms. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying software algorithms. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in software engineering.

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Computer Scientist

Computer Scientists use linear algebra to develop and implement computer algorithms. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying computer algorithms. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in computer science.

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Mathematician

Mathematicians use linear algebra to solve complex mathematical problems. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying mathematical concepts. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in mathematics.

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Physicist

Physicists use linear algebra to model and analyze physical systems. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying physical concepts. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in physics.

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Economist

Economists use linear algebra to model and analyze economic systems. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying economic concepts. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in economics.

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Statistician

Statisticians use linear algebra to analyze data. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying statistical concepts. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in statistics.

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Teacher

Teachers use linear algebra to teach mathematics and other subjects. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and teaching mathematical concepts. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in mathematics education.

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Consultant

Consultants use linear algebra to solve problems in various industries. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying consulting techniques. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in consulting.

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Financial Advisor

Financial Advisors use linear algebra to manage and invest money. This course may be useful for building a foundation in linear algebra, which can be helpful for understanding and applying financial planning techniques. The course covers topics such as vector spaces, linear transformations, and eigenvalues, which are all fundamental concepts in financial planning.

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Linear Algebra Basics

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