Informações sobre o curso
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Aprox. 42 horas para completar

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


Legendas: Inglês, Coreano, Árabe

Habilidades que você terá

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

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Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Aprox. 42 horas para completar

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


Legendas: Inglês, Coreano, Árabe

Programa - O que você aprenderá com este curso

1 horas para concluir


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 vídeos ((Total 27 mín.)), 3 leituras
8 videos
What is this course about?6min
Impact of classification1min
Course overview3min
Outline of first half of course5min
Outline of second half of course5min
Assumed background3min
Let's get started!45s
3 leituras
Important Update regarding the Machine Learning Specialization10min
Slides presented in this module10min
Reading: Software tools you'll need10min
2 horas para concluir

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 vídeos ((Total 78 mín.)), 2 leituras, 2 testes
18 videos
Intuition behind linear classifiers3min
Decision boundaries3min
Linear classifier model5min
Effect of coefficient values on decision boundary2min
Using features of the inputs2min
Predicting class probabilities1min
Review of basics of probabilities6min
Review of basics of conditional probabilities8min
Using probabilities in classification2min
Predicting class probabilities with (generalized) linear models5min
The sigmoid (or logistic) link function4min
Logistic regression model5min
Effect of coefficient values on predicted probabilities7min
Overview of learning logistic regression models2min
Encoding categorical inputs4min
Multiclass classification with 1 versus all7min
Recap of logistic regression classifier1min
2 leituras
Slides presented in this module10min
Predicting sentiment from product reviews10min
2 exercícios práticos
Linear Classifiers & Logistic Regression10min
Predicting sentiment from product reviews24min
2 horas para concluir

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 vídeos ((Total 83 mín.)), 2 leituras, 2 testes
18 videos
Intuition behind maximum likelihood estimation4min
Data likelihood8min
Finding best linear classifier with gradient ascent3min
Review of gradient ascent6min
Learning algorithm for logistic regression3min
Example of computing derivative for logistic regression5min
Interpreting derivative for logistic regression5min
Summary of gradient ascent for logistic regression2min
Choosing step size5min
Careful with step sizes that are too large4min
Rule of thumb for choosing step size3min
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick4min
(VERY OPTIONAL) Expressing the log-likelihood3min
(VERY OPTIONAL) Deriving probability y=-1 given x2min
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form8min
(VERY OPTIONAL) Deriving gradient of log likelihood8min
Recap of learning logistic regression classifiers1min
2 leituras
Slides presented in this module10min
Implementing logistic regression from scratch10min
2 exercícios práticos
Learning Linear Classifiers12min
Implementing logistic regression from scratch16min
2 horas para concluir

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 vídeos ((Total 66 mín.)), 2 leituras, 2 testes
13 videos
Review of overfitting in regression3min
Overfitting in classification5min
Visualizing overfitting with high-degree polynomial features3min
Overfitting in classifiers leads to overconfident predictions5min
Visualizing overconfident predictions4min
(OPTIONAL) Another perspecting on overfitting in logistic regression8min
Penalizing large coefficients to mitigate overfitting5min
L2 regularized logistic regression4min
Visualizing effect of L2 regularization in logistic regression5min
Learning L2 regularized logistic regression with gradient ascent7min
Sparse logistic regression with L1 regularization7min
Recap of overfitting & regularization in logistic regression58s
2 leituras
Slides presented in this module10min
Logistic Regression with L2 regularization10min
2 exercícios práticos
Overfitting & Regularization in Logistic Regression16min
Logistic Regression with L2 regularization16min
2 horas para concluir

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 vídeos ((Total 47 mín.)), 3 leituras, 3 testes
13 videos
Intuition behind decision trees1min
Task of learning decision trees from data3min
Recursive greedy algorithm4min
Learning a decision stump3min
Selecting best feature to split on6min
When to stop recursing4min
Making predictions with decision trees1min
Multiclass classification with decision trees2min
Threshold splits for continuous inputs6min
(OPTIONAL) Picking the best threshold to split on3min
Visualizing decision boundaries5min
Recap of decision trees56s
3 leituras
Slides presented in this module10min
Identifying safe loans with decision trees10min
Implementing binary decision trees10min
3 exercícios práticos
Decision Trees22min
Identifying safe loans with decision trees14min
Implementing binary decision trees14min
2 horas para concluir

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 vídeos ((Total 40 mín.)), 2 leituras, 2 testes
8 videos
Overfitting in decision trees5min
Principle of Occam's razor: Learning simpler decision trees5min
Early stopping in learning decision trees6min
(OPTIONAL) Motivating pruning8min
(OPTIONAL) Pruning decision trees to avoid overfitting6min
(OPTIONAL) Tree pruning algorithm3min
Recap of overfitting and regularization in decision trees1min
2 leituras
Slides presented in this module10min
Decision Trees in Practice10min
2 exercícios práticos
Preventing Overfitting in Decision Trees22min
Decision Trees in Practice28min
1 horas para concluir

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 vídeos ((Total 25 mín.)), 1 leitura, 1 teste
6 videos
Strategy 1: Purification by skipping missing data4min
Strategy 2: Purification by imputing missing data4min
Modifying decision trees to handle missing data4min
Feature split selection with missing data5min
Recap of handling missing data1min
1 leituras
Slides presented in this module10min
1 exercícios práticos
Handling Missing Data14min
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Principais avaliações do Machine Learning: Classification

por 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!

por 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

Sobre Universidade 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....

Sobre o Programa de cursos integrados Aprendizagem Automática

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....
Aprendizagem Automática

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