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Approx. 42 heures pour terminer

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Sous-titres : Anglais, Coréen, Arabe

Compétences que vous acquerrez

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

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Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

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Approx. 42 heures pour terminer

Recommandé : 7 weeks of study, 5-8 hours/week...


Sous-titres : Anglais, Coréen, Arabe

Programme du cours : ce que vous apprendrez dans ce cours

1 heure pour terminer


Classification is one of the most widely used techniques in machine learning, with a broad array of applications, including sentiment analysis, ad targeting, spam detection, risk assessment, medical diagnosis and image classification. The core goal of classification is to predict a category or class y from some inputs x. Through this course, you will become familiar with the fundamental models and algorithms used in classification, as well as a number of core machine learning concepts. Rather than covering all aspects of classification, you will focus on a few core techniques, which are widely used in the real-world to get state-of-the-art performance. By following our hands-on approach, you will implement your own algorithms on multiple real-world tasks, and deeply grasp the core techniques needed to be successful with these approaches in practice. This introduction to the course provides you with an overview of the topics we will cover and the background knowledge and resources we assume you have.

8 vidéos (Total 27 min), 3 lectures
8 vidéos
What is this course about?6 min
Impact of classification1 min
Course overview3 min
Outline of first half of course5 min
Outline of second half of course5 min
Assumed background3 min
Let's get started!45s
3 lectures
Important Update regarding the Machine Learning Specialization10 min
Slides presented in this module10 min
Reading: Software tools you'll need10 min
2 heures pour terminer

Linear Classifiers & Logistic Regression

Linear classifiers are amongst the most practical classification methods. For example, in our sentiment analysis case-study, a linear classifier associates a coefficient with the counts of each word in the sentence. In this module, you will become proficient in this type of representation. You will focus on a particularly useful type of linear classifier called logistic regression, which, in addition to allowing you to predict a class, provides a probability associated with the prediction. These probabilities are extremely useful, since they provide a degree of confidence in the predictions. In this module, you will also be able to construct features from categorical inputs, and to tackle classification problems with more than two class (multiclass problems). You will examine the results of these techniques on a real-world product sentiment analysis task.

18 vidéos (Total 78 min), 2 lectures, 2 quiz
18 vidéos
Intuition behind linear classifiers3 min
Decision boundaries3 min
Linear classifier model5 min
Effect of coefficient values on decision boundary2 min
Using features of the inputs2 min
Predicting class probabilities1 min
Review of basics of probabilities6 min
Review of basics of conditional probabilities8 min
Using probabilities in classification2 min
Predicting class probabilities with (generalized) linear models5 min
The sigmoid (or logistic) link function4 min
Logistic regression model5 min
Effect of coefficient values on predicted probabilities7 min
Overview of learning logistic regression models2 min
Encoding categorical inputs4 min
Multiclass classification with 1 versus all7 min
Recap of logistic regression classifier1 min
2 lectures
Slides presented in this module10 min
Predicting sentiment from product reviews10 min
2 exercices pour s'entraîner
Linear Classifiers & Logistic Regression10 min
Predicting sentiment from product reviews24 min
2 heures pour terminer

Learning Linear Classifiers

Once familiar with linear classifiers and logistic regression, you can now dive in and write your first learning algorithm for classification. In particular, you will use gradient ascent to learn the coefficients of your classifier from data. You first will need to define the quality metric for these tasks using an approach called maximum likelihood estimation (MLE). You will also become familiar with a simple technique for selecting the step size for gradient ascent. An optional, advanced part of this module will cover the derivation of the gradient for logistic regression. You will implement your own learning algorithm for logistic regression from scratch, and use it to learn a sentiment analysis classifier.

18 vidéos (Total 83 min), 2 lectures, 2 quiz
18 vidéos
Intuition behind maximum likelihood estimation4 min
Data likelihood8 min
Finding best linear classifier with gradient ascent3 min
Review of gradient ascent6 min
Learning algorithm for logistic regression3 min
Example of computing derivative for logistic regression5 min
Interpreting derivative for logistic regression5 min
Summary of gradient ascent for logistic regression2 min
Choosing step size5 min
Careful with step sizes that are too large4 min
Rule of thumb for choosing step size3 min
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick4 min
(VERY OPTIONAL) Expressing the log-likelihood3 min
(VERY OPTIONAL) Deriving probability y=-1 given x2 min
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form8 min
(VERY OPTIONAL) Deriving gradient of log likelihood8 min
Recap of learning logistic regression classifiers1 min
2 lectures
Slides presented in this module10 min
Implementing logistic regression from scratch10 min
2 exercices pour s'entraîner
Learning Linear Classifiers12 min
Implementing logistic regression from scratch16 min
2 heures pour terminer

Overfitting & Regularization in Logistic Regression

As we saw in the regression course, overfitting is perhaps the most significant challenge you will face as you apply machine learning approaches in practice. This challenge can be particularly significant for logistic regression, as you will discover in this module, since we not only risk getting an overly complex decision boundary, but your classifier can also become overly confident about the probabilities it predicts. In this module, you will investigate overfitting in classification in significant detail, and obtain broad practical insights from some interesting visualizations of the classifiers' outputs. You will then add a regularization term to your optimization to mitigate overfitting. You will investigate both L2 regularization to penalize large coefficient values, and L1 regularization to obtain additional sparsity in the coefficients. Finally, you will modify your gradient ascent algorithm to learn regularized logistic regression classifiers. You will implement your own regularized logistic regression classifier from scratch, and investigate the impact of the L2 penalty on real-world sentiment analysis data.

13 vidéos (Total 66 min), 2 lectures, 2 quiz
13 vidéos
Review of overfitting in regression3 min
Overfitting in classification5 min
Visualizing overfitting with high-degree polynomial features3 min
Overfitting in classifiers leads to overconfident predictions5 min
Visualizing overconfident predictions4 min
(OPTIONAL) Another perspecting on overfitting in logistic regression8 min
Penalizing large coefficients to mitigate overfitting5 min
L2 regularized logistic regression4 min
Visualizing effect of L2 regularization in logistic regression5 min
Learning L2 regularized logistic regression with gradient ascent7 min
Sparse logistic regression with L1 regularization7 min
Recap of overfitting & regularization in logistic regression58s
2 lectures
Slides presented in this module10 min
Logistic Regression with L2 regularization10 min
2 exercices pour s'entraîner
Overfitting & Regularization in Logistic Regression16 min
Logistic Regression with L2 regularization16 min
2 heures pour terminer

Decision Trees

Along with linear classifiers, decision trees are amongst the most widely used classification techniques in the real world. This method is extremely intuitive, simple to implement and provides interpretable predictions. In this module, you will become familiar with the core decision trees representation. You will then design a simple, recursive greedy algorithm to learn decision trees from data. Finally, you will extend this approach to deal with continuous inputs, a fundamental requirement for practical problems. In this module, you will investigate a brand new case-study in the financial sector: predicting the risk associated with a bank loan. You will implement your own decision tree learning algorithm on real loan data.

13 vidéos (Total 47 min), 3 lectures, 3 quiz
13 vidéos
Intuition behind decision trees1 min
Task of learning decision trees from data3 min
Recursive greedy algorithm4 min
Learning a decision stump3 min
Selecting best feature to split on6 min
When to stop recursing4 min
Making predictions with decision trees1 min
Multiclass classification with decision trees2 min
Threshold splits for continuous inputs6 min
(OPTIONAL) Picking the best threshold to split on3 min
Visualizing decision boundaries5 min
Recap of decision trees56s
3 lectures
Slides presented in this module10 min
Identifying safe loans with decision trees10 min
Implementing binary decision trees10 min
3 exercices pour s'entraîner
Decision Trees22 min
Identifying safe loans with decision trees14 min
Implementing binary decision trees14 min
2 heures pour terminer

Preventing Overfitting in Decision Trees

Out of all machine learning techniques, decision trees are amongst the most prone to overfitting. No practical implementation is possible without including approaches that mitigate this challenge. In this module, through various visualizations and investigations, you will investigate why decision trees suffer from significant overfitting problems. Using the principle of Occam's razor, you will mitigate overfitting by learning simpler trees. At first, you will design algorithms that stop the learning process before the decision trees become overly complex. In an optional segment, you will design a very practical approach that learns an overly-complex tree, and then simplifies it with pruning. Your implementation will investigate the effect of these techniques on mitigating overfitting on our real-world loan data set.

8 vidéos (Total 40 min), 2 lectures, 2 quiz
8 vidéos
Overfitting in decision trees5 min
Principle of Occam's razor: Learning simpler decision trees5 min
Early stopping in learning decision trees6 min
(OPTIONAL) Motivating pruning8 min
(OPTIONAL) Pruning decision trees to avoid overfitting6 min
(OPTIONAL) Tree pruning algorithm3 min
Recap of overfitting and regularization in decision trees1 min
2 lectures
Slides presented in this module10 min
Decision Trees in Practice10 min
2 exercices pour s'entraîner
Preventing Overfitting in Decision Trees22 min
Decision Trees in Practice28 min
1 heure pour terminer

Handling Missing Data

Real-world machine learning problems are fraught with missing data. That is, very often, some of the inputs are not observed for all data points. This challenge is very significant, happens in most cases, and needs to be addressed carefully to obtain great performance. And, this issue is rarely discussed in machine learning courses. In this module, you will tackle the missing data challenge head on. You will start with the two most basic techniques to convert a dataset with missing data into a clean dataset, namely skipping missing values and inputing missing values. In an advanced section, you will also design a modification of the decision tree learning algorithm that builds decisions about missing data right into the model. You will also explore these techniques in your real-data implementation.

6 vidéos (Total 25 min), 1 lecture, 1 quiz
6 vidéos
Strategy 1: Purification by skipping missing data4 min
Strategy 2: Purification by imputing missing data4 min
Modifying decision trees to handle missing data4 min
Feature split selection with missing data5 min
Recap of handling missing data1 min
1 lecture
Slides presented in this module10 min
1 exercice pour s'entraîner
Handling Missing Data14 min
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Principaux examens pour Machine Learning: Classification

par SSOct 16th 2016

Hats off to the team who put the course together! Prof Guestrin is a great teacher. The course gave me in-depth knowledge regarding classification and the math and intuition behind it. It was fun!

par CJJan 25th 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses



Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

Emily Fox

Amazon Professor of Machine Learning

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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