What is linear algebra?

Linear algebra is a central branch of mathematics that is focused on the interaction between vector spaces, linear equations, matrices, and linear transformations. Linear algebra is similar to basic algebra, but instead of finding correlations between single numbers, it seeks to find linear connections between scalars (temperature, mass, volume, speed) and vectors, which are lists of numbers. Linear algebra is important for the worlds of data analytics and any kind of work in the computer software field, as it builds on mathematical logic and building block relationships.

Why is it important to learn linear algebra?

It's important to learn linear algebra to get deeper clarity and better intuition for how mathematical algorithms really work. This knowledge can help you think more logically about projects and apply the linear algebra principles to machine learning projects you may be moving toward. Overall, having linear algebra knowledge can help you learn the fundamentals of data analysis, machine learning, and mathematical theory.

What are typical careers that use linear algebra?

Typical careers that use knowledge of linear algebra involve jobs in physics and science, data analysis, technology operations and management, machine learning, and algebra instruction. All of these careers are in high demand, as the world gets more connected through data systems.

How can taking online courses help me learn linear algebra?

When you take online courses about linear algebra, you can learn how mathematical relationships matter in classifying objects, spaces, and situations. The knowledge you can gain by studying linear algebra can give you insights into different fields like economics, physics, and politics. For example, the concepts of linear algebra can be found in determining local auto traffic depending on people's moving patterns and analyzing how this movement matters to local business factors.

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212 resultados para "linear algebra"

Imperial College London
Mathematics for Machine Learning
Habilidades que obtendrás: Algebra, Algorithms, Analysis, Basic Descriptive Statistics, Calculus, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Linear Algebra, Linearity, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Software Engineering, Software Testing, Statistical Programming, Theoretical Computer Science
4.6
(12.7k reseñas)
Beginner · Specialization
Gratis
Gratis
Gratis
University of Colorado Boulder
Expressway to Data Science: Essential Math
Habilidades que obtendrás: Algebra, Big Data, Calculus, Data Analysis, Data Management, Data Mining, Data Model, Data Structures, Design and Product, Linear Algebra, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Product Design, Software Architecture, Software Engineering, Theoretical Computer Science
4.5
(49 reseñas)
Intermediate · Specialization
MathWorks
Practical Data Science with MATLAB
Habilidades que obtendrás: Analysis, Business Analysis, Computer Graphic Techniques, Computer Graphics, Data Analysis, Data Analysis Software, Data Management, Data Visualization, Exploratory Data Analysis, Extract, Transform, Load, Feature Engineering, General Statistics, Linear Algebra, Machine Learning, Mathematics, Plot (Graphics), Probability & Statistics, Theoretical Computer Science, Web Development
4.7
(922 reseñas)
Beginner · Specialization

Búsquedas relacionadas con linear algebra

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doğrusal cebir i: uzaylar ve işlemciler / linear algebra i: spaces and operators

doğrusal cebir ii: kare matrisler, hesaplama yöntemleri ve uygulamalar / linear algebra ii: square matrices, calculation methods and applications

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En síntesis, estos son los 10 cursos más populares linear algebra

Mathematics for Machine Learning: Imperial College London
Mathematics for Machine Learning: Linear Algebra: Imperial College London
Essential Linear Algebra for Data Science: University of Colorado Boulder
Algebra: Elementary to Advanced: Johns Hopkins University
Mathematics for Engineers: The Hong Kong University of Science and Technology
Matrix Algebra for Engineers: The Hong Kong University of Science and Technology
Math for AI beginner part 1 Linear Algebra: Korea Advanced Institute of Science and Technology(KAIST)
College Math Prep: University of North Texas
Matrix Methods: University of Minnesota
Data Science Math Skills: Duke University

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Preguntas frecuentes sobre Álgebra lineal

Linear algebra is a central branch of mathematics that is focused on the interaction between vector spaces, linear equations, matrices, and linear transformations. Linear algebra is similar to basic algebra, but instead of finding correlations between single numbers, it seeks to find linear connections between scalars (temperature, mass, volume, speed) and vectors, which are lists of numbers. Linear algebra is important for the worlds of data analytics and any kind of work in the computer software field, as it builds on mathematical logic and building block relationships.‎
It's important to learn linear algebra to get deeper clarity and better intuition for how mathematical algorithms really work. This knowledge can help you think more logically about projects and apply the linear algebra principles to machine learning projects you may be moving toward. Overall, having linear algebra knowledge can help you learn the fundamentals of data analysis, machine learning, and mathematical theory.‎
When you take online courses about linear algebra, you can learn how mathematical relationships matter in classifying objects, spaces, and situations. The knowledge you can gain by studying linear algebra can give you insights into different fields like economics, physics, and politics. For example, the concepts of linear algebra can be found in determining local auto traffic depending on people's moving patterns and analyzing how this movement matters to local business factors.‎

Se puso a disposición este contenido de preguntas frecuentes solo con fines informativos. Se les pide a los estudiantes que realicen investigaciones adicionales para asegurarse de que los cursos y otras credenciales cumplan con sus objetivos personales, profesionales y financieros.