課程信息
4.7
2,715 個評分
450 個審閱

第 3 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

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完成時間大約為43 小時

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

英語(English)

字幕:英語(English), 韓語, 阿拉伯語(Arabic)

您將獲得的技能

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

第 3 門課程(共 4 門)

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

完成時間大約為43 小時

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

英語(English)

字幕:英語(English), 韓語, 阿拉伯語(Arabic)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

Welcome!

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 個視頻 (總計 27 分鐘), 3 個閱讀材料
8 個視頻
What is this course about?6分鐘
Impact of classification1分鐘
Course overview3分鐘
Outline of first half of course5分鐘
Outline of second half of course5分鐘
Assumed background3分鐘
Let's get started!45
3 個閱讀材料
Important Update regarding the Machine Learning Specialization10分鐘
Slides presented in this module10分鐘
Reading: Software tools you'll need10分鐘
完成時間為 2 小時

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 個視頻 (總計 78 分鐘), 2 個閱讀材料, 2 個測驗
18 個視頻
Intuition behind linear classifiers3分鐘
Decision boundaries3分鐘
Linear classifier model5分鐘
Effect of coefficient values on decision boundary2分鐘
Using features of the inputs2分鐘
Predicting class probabilities1分鐘
Review of basics of probabilities6分鐘
Review of basics of conditional probabilities8分鐘
Using probabilities in classification2分鐘
Predicting class probabilities with (generalized) linear models5分鐘
The sigmoid (or logistic) link function4分鐘
Logistic regression model5分鐘
Effect of coefficient values on predicted probabilities7分鐘
Overview of learning logistic regression models2分鐘
Encoding categorical inputs4分鐘
Multiclass classification with 1 versus all7分鐘
Recap of logistic regression classifier1分鐘
2 個閱讀材料
Slides presented in this module10分鐘
Predicting sentiment from product reviews10分鐘
2 個練習
Linear Classifiers & Logistic Regression10分鐘
Predicting sentiment from product reviews24分鐘
2
完成時間為 2 小時

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 個視頻 (總計 83 分鐘), 2 個閱讀材料, 2 個測驗
18 個視頻
Intuition behind maximum likelihood estimation4分鐘
Data likelihood8分鐘
Finding best linear classifier with gradient ascent3分鐘
Review of gradient ascent6分鐘
Learning algorithm for logistic regression3分鐘
Example of computing derivative for logistic regression5分鐘
Interpreting derivative for logistic regression5分鐘
Summary of gradient ascent for logistic regression2分鐘
Choosing step size5分鐘
Careful with step sizes that are too large4分鐘
Rule of thumb for choosing step size3分鐘
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick4分鐘
(VERY OPTIONAL) Expressing the log-likelihood3分鐘
(VERY OPTIONAL) Deriving probability y=-1 given x2分鐘
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form8分鐘
(VERY OPTIONAL) Deriving gradient of log likelihood8分鐘
Recap of learning logistic regression classifiers1分鐘
2 個閱讀材料
Slides presented in this module10分鐘
Implementing logistic regression from scratch10分鐘
2 個練習
Learning Linear Classifiers12分鐘
Implementing logistic regression from scratch16分鐘
完成時間為 2 小時

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 個視頻 (總計 66 分鐘), 2 個閱讀材料, 2 個測驗
13 個視頻
Review of overfitting in regression3分鐘
Overfitting in classification5分鐘
Visualizing overfitting with high-degree polynomial features3分鐘
Overfitting in classifiers leads to overconfident predictions5分鐘
Visualizing overconfident predictions4分鐘
(OPTIONAL) Another perspecting on overfitting in logistic regression8分鐘
Penalizing large coefficients to mitigate overfitting5分鐘
L2 regularized logistic regression4分鐘
Visualizing effect of L2 regularization in logistic regression5分鐘
Learning L2 regularized logistic regression with gradient ascent7分鐘
Sparse logistic regression with L1 regularization7分鐘
Recap of overfitting & regularization in logistic regression58
2 個閱讀材料
Slides presented in this module10分鐘
Logistic Regression with L2 regularization10分鐘
2 個練習
Overfitting & Regularization in Logistic Regression16分鐘
Logistic Regression with L2 regularization16分鐘
3
完成時間為 2 小時

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 個視頻 (總計 47 分鐘), 3 個閱讀材料, 3 個測驗
13 個視頻
Intuition behind decision trees1分鐘
Task of learning decision trees from data3分鐘
Recursive greedy algorithm4分鐘
Learning a decision stump3分鐘
Selecting best feature to split on6分鐘
When to stop recursing4分鐘
Making predictions with decision trees1分鐘
Multiclass classification with decision trees2分鐘
Threshold splits for continuous inputs6分鐘
(OPTIONAL) Picking the best threshold to split on3分鐘
Visualizing decision boundaries5分鐘
Recap of decision trees56
3 個閱讀材料
Slides presented in this module10分鐘
Identifying safe loans with decision trees10分鐘
Implementing binary decision trees10分鐘
3 個練習
Decision Trees22分鐘
Identifying safe loans with decision trees14分鐘
Implementing binary decision trees14分鐘
4
完成時間為 2 小時

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 個視頻 (總計 40 分鐘), 2 個閱讀材料, 2 個測驗
8 個視頻
Overfitting in decision trees5分鐘
Principle of Occam's razor: Learning simpler decision trees5分鐘
Early stopping in learning decision trees6分鐘
(OPTIONAL) Motivating pruning8分鐘
(OPTIONAL) Pruning decision trees to avoid overfitting6分鐘
(OPTIONAL) Tree pruning algorithm3分鐘
Recap of overfitting and regularization in decision trees1分鐘
2 個閱讀材料
Slides presented in this module10分鐘
Decision Trees in Practice10分鐘
2 個練習
Preventing Overfitting in Decision Trees22分鐘
Decision Trees in Practice28分鐘
完成時間為 1 小時

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 個視頻 (總計 25 分鐘), 1 個閱讀材料, 1 個測驗
6 個視頻
Strategy 1: Purification by skipping missing data4分鐘
Strategy 2: Purification by imputing missing data4分鐘
Modifying decision trees to handle missing data4分鐘
Feature split selection with missing data5分鐘
Recap of handling missing data1分鐘
1 個閱讀材料
Slides presented in this module10分鐘
1 個練習
Handling Missing Data14分鐘
4.7
450 個審閱Chevron Right

48%

完成這些課程後已開始新的職業生涯

46%

通過此課程獲得實實在在的工作福利

13%

加薪或升職

熱門審閱

創建者 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!

創建者 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

講師

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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

關於 华盛顿大学

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....

關於 机器学习 專項課程

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....
机器学习

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