Informações sobre o curso
4.6
7,913 classificações
1,942 avaliações
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 22 horas para completar

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

Inglês

Legendas: Inglês, Coreano, Vietnamita, Chinês (simplificado)

Habilidades que você terá

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning
Programa de cursos integrados
100% online

100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Horas para completar

Aprox. 22 horas para completar

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

Inglês

Legendas: Inglês, Coreano, Vietnamita, Chinês (simplificado)

Programa - O que você aprenderá com este curso

Semana
1
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 84 mín.) min), 6 leituras
Video18 videos
Who we are5min
Machine learning is changing the world3min
Why a case study approach?7min
Specialization overview6min
How we got into ML3min
Who is this specialization for?4min
What you'll be able to do57s
The capstone and an example intelligent application6min
The future of intelligent applications2min
Starting an IPython Notebook5min
Creating variables in Python7min
Conditional statements and loops in Python8min
Creating functions and lambdas in Python3min
Starting GraphLab Create & loading an SFrame4min
Canvas for data visualization4min
Interacting with columns of an SFrame4min
Using .apply() for data transformation5min
Reading6 leituras
Important Update regarding the Machine Learning Specialization10min
Slides presented in this module10min
Reading: Getting started with Python, IPython Notebook & GraphLab Create10min
Reading: where should my files go?10min
Download the IPython Notebook used in this lesson to follow along10min
Download the IPython Notebook used in this lesson to follow along10min
Semana
2
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 82 mín.) min), 3 leituras, 2 testes
Video19 videos
What is the goal and how might you naively address it?3min
Linear Regression: A Model-Based Approach5min
Adding higher order effects4min
Evaluating overfitting via training/test split6min
Training/test curves4min
Adding other features2min
Other regression examples3min
Regression ML block diagram5min
Loading & exploring house sale data7min
Splitting the data into training and test sets2min
Learning a simple regression model to predict house prices from house size3min
Evaluating error (RMSE) of the simple model2min
Visualizing predictions of simple model with Matplotlib4min
Inspecting the model coefficients learned1min
Exploring other features of the data6min
Learning a model to predict house prices from more features3min
Applying learned models to predict price of an average house5min
Applying learned models to predict price of two fancy houses7min
Reading3 leituras
Slides presented in this module10min
Download the IPython Notebook used in this lesson to follow along10min
Reading: Predicting house prices assignment10min
Quiz2 exercícios práticos
Regression18min
Predicting house prices6min
Semana
3
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 75 mín.) min), 3 leituras, 2 testes
Video19 videos
What is an intelligent restaurant review system?4min
Examples of classification tasks4min
Linear classifiers5min
Decision boundaries3min
Training and evaluating a classifier4min
What's a good accuracy?3min
False positives, false negatives, and confusion matrices6min
Learning curves5min
Class probabilities1min
Classification ML block diagram3min
Loading & exploring product review data2min
Creating the word count vector2min
Exploring the most popular product4min
Defining which reviews have positive or negative sentiment4min
Training a sentiment classifier3min
Evaluating a classifier & the ROC curve4min
Applying model to find most positive & negative reviews for a product4min
Exploring the most positive & negative aspects of a product4min
Reading3 leituras
Slides presented in this module10min
Download the IPython Notebook used in this lesson to follow along10min
Reading: Analyzing product sentiment assignment10min
Quiz2 exercícios práticos
Classification14min
Analyzing product sentiment22min
Semana
4
Horas para completar
2 horas para concluir

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 vídeos (total de (Total 76 mín.) min), 3 leituras, 2 testes
Video17 videos
What is the document retrieval task?1min
Word count representation for measuring similarity6min
Prioritizing important words with tf-idf3min
Calculating tf-idf vectors5min
Retrieving similar documents using nearest neighbor search2min
Clustering documents task overview2min
Clustering documents: An unsupervised learning task4min
k-means: A clustering algorithm3min
Other examples of clustering6min
Clustering and similarity ML block diagram7min
Loading & exploring Wikipedia data5min
Exploring word counts5min
Computing & exploring TF-IDFs7min
Computing distances between Wikipedia articles5min
Building & exploring a nearest neighbors model for Wikipedia articles3min
Examples of document retrieval in action4min
Reading3 leituras
Slides presented in this module10min
Download the IPython Notebook used in this lesson to follow along10min
Reading: Retrieving Wikipedia articles assignment10min
Quiz2 exercícios práticos
Clustering and Similarity12min
Retrieving Wikipedia articles18min
4.6
1,942 avaliaçõesChevron Right
Direcionamento de carreira

31%

comecei uma nova carreira após concluir estes cursos
Benefício de carreira

25%

consegui um benefício significativo de carreira com este curso

Melhores avaliações

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 DPFeb 15th 2016

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

Instrutores

Avatar

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics

Sobre 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....

Sobre o Programa de cursos integrados 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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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