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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197 resultados para "linear algebra"

Imperial College London
Mathematics for Machine Learning
Habilidades que obtendrás: Algebra, Algorithms, Analysis, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Deep Learning, Differential Equations, Dimensionality Reduction, General Statistics, Lambda Calculus, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science
4.6
(12.9k reseñas)
Beginner · Specialization · 3-6 Months
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Imperial College London
Mathematics for Machine Learning: Multivariate Calculus
Habilidades que obtendrás: Mathematics, Linear Algebra, Computer Programming, Deep Learning, Machine Learning, Differential Equations, Algebra, Calculus, Lambda Calculus, Algorithms, Mathematical Optimization, Linear Regression, Linearity, Artificial Neural Networks, Mathematical Theory & Analysis, Machine Learning Algorithms, Theoretical Computer Science, Probability & Statistics, Regression
4.7
(5.2k reseñas)
Beginner · Course · 1-3 Months
Gratis

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

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