Acerca de este Curso
4.6
7,809 calificaciones
1,916 revisiones
Programa Especializado
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Horas para completar

Aprox. 22 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English), Coreano, Vietnamita, Chino (simplificado)...

Habilidades que obtendrás

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning
Programa Especializado
100 % en línea

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.
Horas para completar

Aprox. 22 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English), Coreano, Vietnamita, Chino (simplificado)...

Programa - Qué aprenderás en este curso

Semana
1
Horas para completar
2 horas para completar

Welcome

Machine learning is everywhere, but is often operating behind the scenes. <p>This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.</p>We also discuss who we are, how we got here, and our view of the future of intelligent applications....
Reading
18 videos (Total: 84 min), 6 readings
Video18 videos
Who we are5m
Machine learning is changing the world3m
Why a case study approach?7m
Specialization overview6m
How we got into ML3m
Who is this specialization for?4m
What you'll be able to dom
The capstone and an example intelligent application6m
The future of intelligent applications2m
Starting an IPython Notebook5m
Creating variables in Python7m
Conditional statements and loops in Python8m
Creating functions and lambdas in Python3m
Starting GraphLab Create & loading an SFrame4m
Canvas for data visualization4m
Interacting with columns of an SFrame4m
Using .apply() for data transformation5m
Reading6 lecturas
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Reading: Getting started with Python, IPython Notebook & GraphLab Create10m
Reading: where should my files go?10m
Download the IPython Notebook used in this lesson to follow along10m
Download the IPython Notebook used in this lesson to follow along10m
Semana
2
Horas para completar
2 horas para completar

Regression: Predicting House Prices

This week you will build your first intelligent application that makes predictions from data.<p>We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). <p>This is just one of the many places where regression can be applied.Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.</p>You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook....
Reading
19 videos (Total: 82 min), 3 readings, 2 quizzes
Video19 videos
What is the goal and how might you naively address it?3m
Linear Regression: A Model-Based Approach5m
Adding higher order effects4m
Evaluating overfitting via training/test split6m
Training/test curves4m
Adding other features2m
Other regression examples3m
Regression ML block diagram5m
Loading & exploring house sale data7m
Splitting the data into training and test sets2m
Learning a simple regression model to predict house prices from house size3m
Evaluating error (RMSE) of the simple model2m
Visualizing predictions of simple model with Matplotlib4m
Inspecting the model coefficients learned1m
Exploring other features of the data6m
Learning a model to predict house prices from more features3m
Applying learned models to predict price of an average house5m
Applying learned models to predict price of two fancy houses7m
Reading3 lecturas
Slides presented in this module10m
Download the IPython Notebook used in this lesson to follow along10m
Reading: Predicting house prices assignment10m
Quiz2 ejercicios de práctica
Regression18m
Predicting house prices6m
Semana
3
Horas para completar
2 horas para completar

Classification: Analyzing Sentiment

How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote?<p>In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).This task is an example of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification.</p>You will analyze the accuracy of your classifier, implement an actual classifier in an iPython notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone. ...
Reading
19 videos (Total: 75 min), 3 readings, 2 quizzes
Video19 videos
What is an intelligent restaurant review system?4m
Examples of classification tasks4m
Linear classifiers5m
Decision boundaries3m
Training and evaluating a classifier4m
What's a good accuracy?3m
False positives, false negatives, and confusion matrices6m
Learning curves5m
Class probabilities1m
Classification ML block diagram3m
Loading & exploring product review data2m
Creating the word count vector2m
Exploring the most popular product4m
Defining which reviews have positive or negative sentiment4m
Training a sentiment classifier3m
Evaluating a classifier & the ROC curve4m
Applying model to find most positive & negative reviews for a product4m
Exploring the most positive & negative aspects of a product4m
Reading3 lecturas
Slides presented in this module10m
Download the IPython Notebook used in this lesson to follow along10m
Reading: Analyzing product sentiment assignment10m
Quiz2 ejercicios de práctica
Classification14m
Analyzing product sentiment22m
Semana
4
Horas para completar
2 horas para completar

Clustering and Similarity: Retrieving Documents

A reader is interested in a specific news article and you want to find a similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place?<p>In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic).</p>You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook....
Reading
17 videos (Total: 76 min), 3 readings, 2 quizzes
Video17 videos
What is the document retrieval task?1m
Word count representation for measuring similarity6m
Prioritizing important words with tf-idf3m
Calculating tf-idf vectors5m
Retrieving similar documents using nearest neighbor search2m
Clustering documents task overview2m
Clustering documents: An unsupervised learning task4m
k-means: A clustering algorithm3m
Other examples of clustering6m
Clustering and similarity ML block diagram7m
Loading & exploring Wikipedia data5m
Exploring word counts5m
Computing & exploring TF-IDFs7m
Computing distances between Wikipedia articles5m
Building & exploring a nearest neighbors model for Wikipedia articles3m
Examples of document retrieval in action4m
Reading3 lecturas
Slides presented in this module10m
Download the IPython Notebook used in this lesson to follow along10m
Reading: Retrieving Wikipedia articles assignment10m
Quiz2 ejercicios de práctica
Clustering and Similarity12m
Retrieving Wikipedia articles18m
4.6
Dirección de la carrera

31%

comenzó una nueva carrera después de completar estos cursos
Beneficio de la carrera

83%

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

Principales revisiones

por BLOct 17th 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

por DSSep 28th 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

Instructores

Avatar

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics

Acerca de University of 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 del programa especializado Machine Learning

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....
Machine Learning

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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