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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202 resultados referentes a "linear algebra"

Imperial College London
Mathematics for Machine Learning
Habilidades que você terá: 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
(12.6k avaliações)
Beginner · Specialization · 3+ Months
Gratuito
Gratuito
Gratuito
Stanford University
Machine Learning
Habilidades que você terá: Distributed Computing Architecture, Deep Learning, Computer Architecture, Statistical Machine Learning, Computer Programming, Theoretical Computer Science, Algorithms, Machine Learning Algorithms, Applied Machine Learning, Data Analysis, Computer Vision, Differential Equations, Artificial Neural Networks, Other Programming Languages, Estimation, Calculus, General Statistics, Dimensionality Reduction, Probability Distribution, Probability & Statistics, Linear Algebra, Security Engineering, Network Security, Data Mining, Econometrics, Data Analysis Software, Mathematics, Natural Language Processing, Feature Engineering, Geostatistics, Machine Learning, Support Vector Machine, Computer Networking, Regression
4.9
(170k avaliações)
Mixed · Course · 3+ Months
SAS
SAS Statistical Business Analyst
Habilidades que você terá: Advertising, Algebra, Analysis, Business Analysis, Communication, Computer Programming, Data Analysis, Econometrics, Experiment, General Statistics, Hypothesis, Hypothesis Testing, Linear Regression, Machine Learning, Marketing, Mathematics, Modeling, Probability & Statistics, Python Programming, Regression, Regression Analysis, SAS (Software), Statistical Analysis, Statistical Programming, Supply Chain, Supply Chain Systems, Supply Chain and Logistics
4.6
(86 avaliações)
Intermediate · Professional Certificate · 3+ Months

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

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Em resumo, aqui estão 10 dos nossos cursos mais 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
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)
Mathematics for Engineers: The Hong Kong University of Science and Technology
Matrix Methods: University of Minnesota
Introduction to Electricity and Magnetism: Rice University
Algebra: Elementary to Advanced: Johns Hopkins University
Data Science Math Skills: Duke University

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Perguntas frequentes sobre Álgebra linear

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.‎

Este conteúdo de Perguntas frequentes foi disponibilizado apenas para fins informativos. Os aprendizes são aconselhados a realizar pesquisas adicionais para garantir que os cursos e outras credenciais buscadas atendam a seus objetivos pessoais, profissionais e financeiros.