Acerca de este Curso

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Resultados profesionales del estudiante

37%

comenzó una nueva carrera después de completar estos cursos

38%

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.
Aprox. 17 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano, Árabe (Arabic)

Habilidades que obtendrás

Data Clustering AlgorithmsK-Means ClusteringMachine LearningK-D Tree

Resultados profesionales del estudiante

37%

comenzó una nueva carrera después de completar estos cursos

38%

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.
Aprox. 17 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Coreano, Árabe (Arabic)

ofrecido por

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Universidad de Washington

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up91%(5,734 calificaciones)Info
Semana
1

Semana 1

1 hora para completar

Welcome

1 hora para completar
4 videos (Total 25 minutos), 4 lecturas
4 videos
Course overview3m
Module-by-module topics covered8m
Assumed background6m
4 lecturas
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Software tools you'll need for this course10m
A big week ahead!10m
Semana
2

Semana 2

5 horas para completar

Nearest Neighbor Search

5 horas para completar
22 videos (Total 137 minutos), 4 lecturas, 5 cuestionarios
22 videos
1-NN algorithm2m
k-NN algorithm6m
Document representation5m
Distance metrics: Euclidean and scaled Euclidean6m
Writing (scaled) Euclidean distance using (weighted) inner products4m
Distance metrics: Cosine similarity9m
To normalize or not and other distance considerations6m
Complexity of brute force search1m
KD-tree representation9m
NN search with KD-trees7m
Complexity of NN search with KD-trees5m
Visualizing scaling behavior of KD-trees4m
Approximate k-NN search using KD-trees7m
Limitations of KD-trees3m
LSH as an alternative to KD-trees4m
Using random lines to partition points5m
Defining more bins3m
Searching neighboring bins8m
LSH in higher dimensions4m
(OPTIONAL) Improving efficiency through multiple tables22m
A brief recap2m
4 lecturas
Slides presented in this module10m
Choosing features and metrics for nearest neighbor search10m
(OPTIONAL) A worked-out example for KD-trees10m
Implementing Locality Sensitive Hashing from scratch10m
5 ejercicios de práctica
Representations and metrics30m
Choosing features and metrics for nearest neighbor search30m
KD-trees30m
Locality Sensitive Hashing30m
Implementing Locality Sensitive Hashing from scratch30m
Semana
3

Semana 3

3 horas para completar

Clustering with k-means

3 horas para completar
13 videos (Total 79 minutos), 2 lecturas, 3 cuestionarios
13 videos
An unsupervised task6m
Hope for unsupervised learning, and some challenge cases4m
The k-means algorithm7m
k-means as coordinate descent6m
Smart initialization via k-means++4m
Assessing the quality and choosing the number of clusters9m
Motivating MapReduce8m
The general MapReduce abstraction5m
MapReduce execution overview and combiners6m
MapReduce for k-means7m
Other applications of clustering7m
A brief recap1m
2 lecturas
Slides presented in this module10m
Clustering text data with k-means10m
3 ejercicios de práctica
k-means30m
Clustering text data with K-means16m
MapReduce for k-means30m
Semana
4

Semana 4

4 horas para completar

Mixture Models

4 horas para completar
15 videos (Total 91 minutos), 4 lecturas, 3 cuestionarios
15 videos
Aggregating over unknown classes in an image dataset6m
Univariate Gaussian distributions2m
Bivariate and multivariate Gaussians7m
Mixture of Gaussians6m
Interpreting the mixture of Gaussian terms5m
Scaling mixtures of Gaussians for document clustering5m
Computing soft assignments from known cluster parameters7m
(OPTIONAL) Responsibilities as Bayes' rule5m
Estimating cluster parameters from known cluster assignments6m
Estimating cluster parameters from soft assignments8m
EM iterates in equations and pictures6m
Convergence, initialization, and overfitting of EM9m
Relationship to k-means3m
A brief recap1m
4 lecturas
Slides presented in this module10m
(OPTIONAL) A worked-out example for EM10m
Implementing EM for Gaussian mixtures10m
Clustering text data with Gaussian mixtures10m
3 ejercicios de práctica
EM for Gaussian mixtures30m
Implementing EM for Gaussian mixtures30m
Clustering text data with Gaussian mixtures30m

Reseñas

Principales reseñas sobre MACHINE LEARNING: CLUSTERING & RETRIEVAL

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Acerca de Programa especializado: Aprendizaje Automático

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
Aprendizaje Automático

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