课程信息

160,852 次近期查看

学生职业成果

46%

完成这些课程后已开始新的职业生涯

48%

通过此课程获得实实在在的工作福利

19%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 3 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为18 小时
英语(English)
字幕:英语(English), 韩语, 阿拉伯语(Arabic)

您将获得的技能

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

学生职业成果

46%

完成这些课程后已开始新的职业生涯

48%

通过此课程获得实实在在的工作福利

19%

加薪或升职
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 3 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为18 小时
英语(English)
字幕:英语(English), 韩语, 阿拉伯语(Arabic)

提供方

华盛顿大学 徽标

华盛顿大学

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up94%(9,727 个评分)Info
1

1

完成时间为 1 小时

Welcome!

完成时间为 1 小时
8 个视频 (总计 27 分钟), 3 个阅读材料
8 个视频
What is this course about?6分钟
Impact of classification1分钟
Course overview3分钟
Outline of first half of course5分钟
Outline of second half of course5分钟
Assumed background3分钟
Let's get started!45
3 个阅读材料
Important Update regarding the Machine Learning Specialization10分钟
Slides presented in this module10分钟
Reading: Software tools you'll need10分钟
完成时间为 2 小时

Linear Classifiers & Logistic Regression

完成时间为 2 小时
18 个视频 (总计 78 分钟), 2 个阅读材料, 2 个测验
18 个视频
Intuition behind linear classifiers3分钟
Decision boundaries3分钟
Linear classifier model5分钟
Effect of coefficient values on decision boundary2分钟
Using features of the inputs2分钟
Predicting class probabilities1分钟
Review of basics of probabilities6分钟
Review of basics of conditional probabilities8分钟
Using probabilities in classification2分钟
Predicting class probabilities with (generalized) linear models5分钟
The sigmoid (or logistic) link function4分钟
Logistic regression model5分钟
Effect of coefficient values on predicted probabilities7分钟
Overview of learning logistic regression models2分钟
Encoding categorical inputs4分钟
Multiclass classification with 1 versus all7分钟
Recap of logistic regression classifier1分钟
2 个阅读材料
Slides presented in this module10分钟
Predicting sentiment from product reviews10分钟
2 个练习
Linear Classifiers & Logistic Regression10分钟
Predicting sentiment from product reviews24分钟
2

2

完成时间为 2 小时

Learning Linear Classifiers

完成时间为 2 小时
18 个视频 (总计 83 分钟), 2 个阅读材料, 2 个测验
18 个视频
Intuition behind maximum likelihood estimation4分钟
Data likelihood8分钟
Finding best linear classifier with gradient ascent3分钟
Review of gradient ascent6分钟
Learning algorithm for logistic regression3分钟
Example of computing derivative for logistic regression5分钟
Interpreting derivative for logistic regression5分钟
Summary of gradient ascent for logistic regression2分钟
Choosing step size5分钟
Careful with step sizes that are too large4分钟
Rule of thumb for choosing step size3分钟
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick4分钟
(VERY OPTIONAL) Expressing the log-likelihood3分钟
(VERY OPTIONAL) Deriving probability y=-1 given x2分钟
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form8分钟
(VERY OPTIONAL) Deriving gradient of log likelihood8分钟
Recap of learning logistic regression classifiers1分钟
2 个阅读材料
Slides presented in this module10分钟
Implementing logistic regression from scratch10分钟
2 个练习
Learning Linear Classifiers12分钟
Implementing logistic regression from scratch16分钟
完成时间为 2 小时

Overfitting & Regularization in Logistic Regression

完成时间为 2 小时
13 个视频 (总计 66 分钟), 2 个阅读材料, 2 个测验
13 个视频
Review of overfitting in regression3分钟
Overfitting in classification5分钟
Visualizing overfitting with high-degree polynomial features3分钟
Overfitting in classifiers leads to overconfident predictions5分钟
Visualizing overconfident predictions4分钟
(OPTIONAL) Another perspecting on overfitting in logistic regression8分钟
Penalizing large coefficients to mitigate overfitting5分钟
L2 regularized logistic regression4分钟
Visualizing effect of L2 regularization in logistic regression5分钟
Learning L2 regularized logistic regression with gradient ascent7分钟
Sparse logistic regression with L1 regularization7分钟
Recap of overfitting & regularization in logistic regression58
2 个阅读材料
Slides presented in this module10分钟
Logistic Regression with L2 regularization10分钟
2 个练习
Overfitting & Regularization in Logistic Regression16分钟
Logistic Regression with L2 regularization16分钟
3

3

完成时间为 2 小时

Decision Trees

完成时间为 2 小时
13 个视频 (总计 47 分钟), 3 个阅读材料, 3 个测验
13 个视频
Intuition behind decision trees1分钟
Task of learning decision trees from data3分钟
Recursive greedy algorithm4分钟
Learning a decision stump3分钟
Selecting best feature to split on6分钟
When to stop recursing4分钟
Making predictions with decision trees1分钟
Multiclass classification with decision trees2分钟
Threshold splits for continuous inputs6分钟
(OPTIONAL) Picking the best threshold to split on3分钟
Visualizing decision boundaries5分钟
Recap of decision trees56
3 个阅读材料
Slides presented in this module10分钟
Identifying safe loans with decision trees10分钟
Implementing binary decision trees10分钟
3 个练习
Decision Trees22分钟
Identifying safe loans with decision trees14分钟
Implementing binary decision trees14分钟
4

4

完成时间为 2 小时

Preventing Overfitting in Decision Trees

完成时间为 2 小时
8 个视频 (总计 40 分钟), 2 个阅读材料, 2 个测验
8 个视频
Overfitting in decision trees5分钟
Principle of Occam's razor: Learning simpler decision trees5分钟
Early stopping in learning decision trees6分钟
(OPTIONAL) Motivating pruning8分钟
(OPTIONAL) Pruning decision trees to avoid overfitting6分钟
(OPTIONAL) Tree pruning algorithm3分钟
Recap of overfitting and regularization in decision trees1分钟
2 个阅读材料
Slides presented in this module10分钟
Decision Trees in Practice10分钟
2 个练习
Preventing Overfitting in Decision Trees22分钟
Decision Trees in Practice28分钟
完成时间为 1 小时

Handling Missing Data

完成时间为 1 小时
6 个视频 (总计 25 分钟), 1 个阅读材料, 1 个测验
6 个视频
Strategy 1: Purification by skipping missing data4分钟
Strategy 2: Purification by imputing missing data4分钟
Modifying decision trees to handle missing data4分钟
Feature split selection with missing data5分钟
Recap of handling missing data1分钟
1 个阅读材料
Slides presented in this module10分钟
1 个练习
Handling Missing Data14分钟

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关于 机器学习 专项课程

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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