Acerca de este Curso

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Resultados profesionales del estudiante

50%

comenzó una nueva carrera después de completar estos cursos

48%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
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
Aprox. 20 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Habilidades que obtendrás

Dimensionality ReductionPython ProgrammingLinear Algebra

Resultados profesionales del estudiante

50%

comenzó una nueva carrera después de completar estos cursos

48%

consiguió un beneficio tangible en su carrera profesional gracias a este curso
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
Aprox. 20 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

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Imperial College London

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up80%(4,907 calificaciones)Info
Semana
1

Semana 1

6 horas para completar

Statistics of Datasets

6 horas para completar
8 videos (Total 27 minutos), 7 lecturas, 4 cuestionarios
8 videos
Welcome to module 141s
Mean of a dataset4m
Variance of one-dimensional datasets4m
Variance of higher-dimensional datasets5m
Effect on the mean4m
Effect on the (co)variance3m
See you next module!27s
7 lecturas
About Imperial College & the team5m
How to be successful in this course5m
Grading policy5m
Additional readings & helpful references10m
Mini numpy tutorial1h
Set up Jupyter notebook environment offline10m
Symmetric, positive definite matrices10m
3 ejercicios de práctica
Mean of datasets15m
Variance of 1D datasets15m
Covariance matrix of a two-dimensional dataset15m
Semana
2

Semana 2

4 horas para completar

Inner Products

4 horas para completar
8 videos (Total 36 minutos), 1 lectura, 5 cuestionarios
8 videos
Dot product4m
Inner product: definition5m
Inner product: length of vectors7m
Inner product: distances between vectors3m
Inner product: angles and orthogonality5m
Inner products of functions and random variables (optional)7m
Heading for the next module!35s
1 lectura
Basis vectors20m
4 ejercicios de práctica
Dot product30m
Properties of inner products20m
General inner products: lengths and distances20m
Angles between vectors using a non-standard inner product20m
Semana
3

Semana 3

4 horas para completar

Orthogonal Projections

4 horas para completar
6 videos (Total 25 minutos), 1 lectura, 3 cuestionarios
6 videos
Projection onto 1D subspaces7m
Example: projection onto 1D subspaces3m
Projections onto higher-dimensional subspaces8m
Example: projection onto a 2D subspace3m
This was module 3!32s
1 lectura
Full derivation of the projection20m
2 ejercicios de práctica
Projection onto a 1-dimensional subspace25m
Project 3D data onto a 2D subspace40m
Semana
4

Semana 4

5 horas para completar

Principal Component Analysis

5 horas para completar
10 videos (Total 52 minutos), 5 lecturas, 2 cuestionarios
10 videos
Problem setting and PCA objective7m
Finding the coordinates of the projected data5m
Reformulation of the objective10m
Finding the basis vectors that span the principal subspace7m
Steps of PCA4m
PCA in high dimensions5m
Other interpretations of PCA (optional)7m
Summary of this module42s
This was the course on PCA56s
5 lecturas
Vector spaces20m
Orthogonal complements10m
Multivariate chain rule10m
Lagrange multipliers10m
Did you like the course? Let us know!10m
1 ejercicio de práctica
Chain rule practice20m

Reseñas

Principales reseñas sobre MATHEMATICS FOR MACHINE LEARNING: PCA

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Acerca de Programa especializado: Matemática aplicada al aprendizaje automático

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning....
Matemática aplicada al aprendizaje automático

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