Acerca de este Curso

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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Some background in Python programming language and algebra.

Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio

Some background in Python programming language and algebra.

Aprox. 14 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

Logotipo de National Research University Higher School of Economics

National Research University Higher School of Economics

Comienza a trabajar para obtener tu maestría

Este curso es parte del Master of Data Science completamente en línea de National Research University Higher School of Economics. Si eres aceptado en el programa completo, tus cursos cuentan para tu título.

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

5 horas para completar

Systems of linear equations and linear classifier

5 horas para completar
15 videos (Total 118 minutos), 2 lecturas, 2 cuestionarios
15 videos
Introduction to Linear Algebra42s
Linear Algebra and Calculus4m
Matrices and Multidimensional Vectors10m
Matrix arithmetics6m
Properties of matrix operations and some special matrices10m
Vectors and matrices in Python4m
Systems of linear equations11m
Matrix inverse13m
Gaussian elimination. The first example4m
Elementary row operations6m
Gaussian elimination. Main theorem.5m
Gaussian Elimination. The algorithm.13m
The Inverse matrix with Gaussian elimination5m
LU and PLU decomposition17m
2 lecturas
About the University10m
Covered Python methods20m
1 ejercicio de práctica
Week 11h
Semana
2

Semana 2

2 horas para completar

Full rank decomposition and systems of linear equations

2 horas para completar
14 videos (Total 86 minutos)
14 videos
Abstract algebra and linear algebra11m
Axioms of vector spaces: first application6m
Examples of vector spaces8m
Subspaces1m
Linear combinations and spans2m
Basis and linear dependence7m
Dimension of a vector space5m
Examples of bases7m
Linear dependence and rank3m
Formula for the solution of a SLAE9m
An example of vector representation of the set of solutions7m
Rouché–Capelli Theorem4m
Full rank decomposition8m
1 ejercicio de práctica
Week 230m
Semana
3

Semana 3

2 horas para completar

Euclidean spaces

2 horas para completar
10 videos (Total 85 minutos)
10 videos
Coordinates change example9m
Euclidean space8m
Geometry and Euclidean spaces1m
Orthogonal and orthonormal bases4m
Distance and orthogonal projections6m
Inconsistent systems and the least squares method12m
Linear regression example8m
Introduction to support vector machine16m
Linear regression and SVM with Python4m
1 ejercicio de práctica
Week 330m
Semana
4

Semana 4

4 horas para completar

Final Project

4 horas para completar
1 video (Total 2 minutos), 1 lectura, 2 cuestionarios
1 lectura
References and further reading10m
1 ejercicio de práctica
Life expectancy prediction quiz1h

Revisiones

Principales revisiones sobre FIRST STEPS IN LINEAR ALGEBRA FOR MACHINE LEARNING

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Acerca de Programa especializado: Mathematics for Data Science

Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python....
Mathematics for Data Science

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