À propos de ce cours
4.6
8,261 notes
2,003 avis

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Approx. 24 heures pour terminer

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

Anglais

Sous-titres : Anglais, Coréen, Vietnamien, Chinois (simplifié)

Compétences que vous acquerrez

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Approx. 24 heures pour terminer

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

Anglais

Sous-titres : Anglais, Coréen, Vietnamien, Chinois (simplifié)

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
2 heures pour terminer

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....
18 vidéos (Total 84 min), 6 lectures
18 vidéos
Who we are5 min
Machine learning is changing the world3 min
Why a case study approach?7 min
Specialization overview6 min
How we got into ML3 min
Who is this specialization for?4 min
What you'll be able to do57s
The capstone and an example intelligent application6 min
The future of intelligent applications2 min
Starting an IPython Notebook5 min
Creating variables in Python7 min
Conditional statements and loops in Python8 min
Creating functions and lambdas in Python3 min
Starting GraphLab Create & loading an SFrame4 min
Canvas for data visualization4 min
Interacting with columns of an SFrame4 min
Using .apply() for data transformation5 min
6 lectures
Important Update regarding the Machine Learning Specialization10 min
Slides presented in this module10 min
Reading: Getting started with Python, IPython Notebook & GraphLab Create10 min
Reading: where should my files go?10 min
Download the IPython Notebook used in this lesson to follow along10 min
Download the IPython Notebook used in this lesson to follow along10 min
Semaine
2
2 heures pour terminer

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....
19 vidéos (Total 82 min), 3 lectures, 2 quiz
19 vidéos
What is the goal and how might you naively address it?3 min
Linear Regression: A Model-Based Approach5 min
Adding higher order effects4 min
Evaluating overfitting via training/test split6 min
Training/test curves4 min
Adding other features2 min
Other regression examples3 min
Regression ML block diagram5 min
Loading & exploring house sale data7 min
Splitting the data into training and test sets2 min
Learning a simple regression model to predict house prices from house size3 min
Evaluating error (RMSE) of the simple model2 min
Visualizing predictions of simple model with Matplotlib4 min
Inspecting the model coefficients learned1 min
Exploring other features of the data6 min
Learning a model to predict house prices from more features3 min
Applying learned models to predict price of an average house5 min
Applying learned models to predict price of two fancy houses7 min
3 lectures
Slides presented in this module10 min
Download the IPython Notebook used in this lesson to follow along10 min
Reading: Predicting house prices assignment10 min
2 exercices pour s'entraîner
Regression18 min
Predicting house prices6 min
Semaine
3
2 heures pour terminer

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. ...
19 vidéos (Total 75 min), 3 lectures, 2 quiz
19 vidéos
What is an intelligent restaurant review system?4 min
Examples of classification tasks4 min
Linear classifiers5 min
Decision boundaries3 min
Training and evaluating a classifier4 min
What's a good accuracy?3 min
False positives, false negatives, and confusion matrices6 min
Learning curves5 min
Class probabilities1 min
Classification ML block diagram3 min
Loading & exploring product review data2 min
Creating the word count vector2 min
Exploring the most popular product4 min
Defining which reviews have positive or negative sentiment4 min
Training a sentiment classifier3 min
Evaluating a classifier & the ROC curve4 min
Applying model to find most positive & negative reviews for a product4 min
Exploring the most positive & negative aspects of a product4 min
3 lectures
Slides presented in this module10 min
Download the IPython Notebook used in this lesson to follow along10 min
Reading: Analyzing product sentiment assignment10 min
2 exercices pour s'entraîner
Classification14 min
Analyzing product sentiment22 min
Semaine
4
2 heures pour terminer

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....
17 vidéos (Total 76 min), 3 lectures, 2 quiz
17 vidéos
What is the document retrieval task?1 min
Word count representation for measuring similarity6 min
Prioritizing important words with tf-idf3 min
Calculating tf-idf vectors5 min
Retrieving similar documents using nearest neighbor search2 min
Clustering documents task overview2 min
Clustering documents: An unsupervised learning task4 min
k-means: A clustering algorithm3 min
Other examples of clustering6 min
Clustering and similarity ML block diagram7 min
Loading & exploring Wikipedia data5 min
Exploring word counts5 min
Computing & exploring TF-IDFs7 min
Computing distances between Wikipedia articles5 min
Building & exploring a nearest neighbors model for Wikipedia articles3 min
Examples of document retrieval in action4 min
3 lectures
Slides presented in this module10 min
Download the IPython Notebook used in this lesson to follow along10 min
Reading: Retrieving Wikipedia articles assignment10 min
2 exercices pour s'entraîner
Clustering and Similarity12 min
Retrieving Wikipedia articles18 min
4.6
2,003 avisChevron Right

33%

a commencé une nouvelle carrière après avoir terminé ces cours

29%

a bénéficié d'un avantage concret dans sa carrière grâce à ce cours

Meilleurs avis

par 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

par SZDec 20th 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

Enseignants

Avatar

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics

À propos de Université 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....

À propos de la Spécialisation Apprentissage automatique

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....
Apprentissage automatique

Foire Aux Questions

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