Acerca de este Curso
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Fechas límite flexibles

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Aprox. 48 horas para completar

Sugerido: 6 weeks of study, 5-8 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano, Árabe (Arabic)

Habilidades que obtendrás

Data Clustering AlgorithmsK-Means ClusteringMachine LearningK-D Tree

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. 48 horas para completar

Sugerido: 6 weeks of study, 5-8 hours/week...

Inglés (English)

Subtítulos: Inglés (English), Coreano, Árabe (Arabic)

Los estudiantes que toman este Course son

  • Data Scientists
  • Machine Learning Engineers
  • Biostatisticians
  • Systems Analysts
  • Data Engineers

Programa - Qué aprenderás en este curso

Semana
1
1 hora para completar

Welcome

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
4 horas para completar

Nearest Neighbor Search

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 metrics12m
Choosing features and metrics for nearest neighbor search10m
KD-trees10m
Locality Sensitive Hashing10m
Implementing Locality Sensitive Hashing from scratch10m
Semana
3
2 horas para completar

Clustering with k-means

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-means18m
Clustering text data with K-means16m
MapReduce for k-means10m
Semana
4
3 horas para completar

Mixture Models

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 mixtures18m
Implementing EM for Gaussian mixtures12m
Clustering text data with Gaussian mixtures8m
4.6
299 revisionesChevron Right

35%

comenzó una nueva carrera después de completar estos cursos

37%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones sobre Machine Learning: Clustering & Retrieval

por JMJan 17th 2017

Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.

por BKAug 25th 2016

excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.

Instructores

Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

Acerca de Universidad de Washington

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

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

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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