Informações sobre o curso
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Prazos flexíveis

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Aprox. 41 horas para completar

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

Inglês

Legendas: Inglês, Coreano, Árabe

Habilidades que você terá

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Aprox. 41 horas para completar

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

Inglês

Legendas: Inglês, Coreano, Árabe

Programa - O que você aprenderá com este curso

Semana
1
1 hora para concluir

Welcome!

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

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
Semana
2
2 horas para concluir

Learning Linear Classifiers

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

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
Semana
3
2 horas para concluir

Decision Trees

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
Semana
4
2 horas para concluir

Preventing Overfitting in Decision Trees

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 hora para concluir

Handling Missing Data

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 exercício prático
Handling Missing Data14min
4.7
482 avaliaçõesChevron Right

46%

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19%

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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

Instrutores

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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
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Emily Fox

Amazon Professor of Machine Learning
Statistics

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 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

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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