课程信息
4.6
7,897 个评分
1,936 个审阅
专项课程

第 1 门课程(共 4 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
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完成时间(小时)

完成时间大约为22 小时

建议:6 weeks of study, 5-8 hours/week...
可选语言

英语(English)

字幕:英语(English), 韩语, 越南语, 中文(简体)

您将获得的技能

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning
专项课程

第 1 门课程(共 4 门)

100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

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(小时)

完成时间大约为22 小时

建议:6 weeks of study, 5-8 hours/week...
可选语言

英语(English)

字幕:英语(English), 韩语, 越南语, 中文(简体)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 2 小时

Welcome

Machine learning is everywhere, but is often operating behind the scenes. <p>This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.</p>We also discuss who we are, how we got here, and our view of the future of intelligent applications....
Reading
18 个视频 (总计 84 分钟), 6 个阅读材料
Video18 个视频
Who we are5分钟
Machine learning is changing the world3分钟
Why a case study approach?7分钟
Specialization overview6分钟
How we got into ML3分钟
Who is this specialization for?4分钟
What you'll be able to do分钟
The capstone and an example intelligent application6分钟
The future of intelligent applications2分钟
Starting an IPython Notebook5分钟
Creating variables in Python7分钟
Conditional statements and loops in Python8分钟
Creating functions and lambdas in Python3分钟
Starting GraphLab Create & loading an SFrame4分钟
Canvas for data visualization4分钟
Interacting with columns of an SFrame4分钟
Using .apply() for data transformation5分钟
Reading6 个阅读材料
Important Update regarding the Machine Learning Specialization10分钟
Slides presented in this module10分钟
Reading: Getting started with Python, IPython Notebook & GraphLab Create10分钟
Reading: where should my files go?10分钟
Download the IPython Notebook used in this lesson to follow along10分钟
Download the IPython Notebook used in this lesson to follow along10分钟
2
完成时间(小时)
完成时间为 2 小时

Regression: Predicting House Prices

This week you will build your first intelligent application that makes predictions from data.<p>We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). <p>This is just one of the many places where regression can be applied.Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.</p>You will also examine how to analyze the performance of your predictive model and implement regression in practice using an iPython notebook....
Reading
19 个视频 (总计 82 分钟), 3 个阅读材料, 2 个测验
Video19 个视频
What is the goal and how might you naively address it?3分钟
Linear Regression: A Model-Based Approach5分钟
Adding higher order effects4分钟
Evaluating overfitting via training/test split6分钟
Training/test curves4分钟
Adding other features2分钟
Other regression examples3分钟
Regression ML block diagram5分钟
Loading & exploring house sale data7分钟
Splitting the data into training and test sets2分钟
Learning a simple regression model to predict house prices from house size3分钟
Evaluating error (RMSE) of the simple model2分钟
Visualizing predictions of simple model with Matplotlib4分钟
Inspecting the model coefficients learned1分钟
Exploring other features of the data6分钟
Learning a model to predict house prices from more features3分钟
Applying learned models to predict price of an average house5分钟
Applying learned models to predict price of two fancy houses7分钟
Reading3 个阅读材料
Slides presented in this module10分钟
Download the IPython Notebook used in this lesson to follow along10分钟
Reading: Predicting house prices assignment10分钟
Quiz2 个练习
Regression18分钟
Predicting house prices6分钟
3
完成时间(小时)
完成时间为 2 小时

Classification: Analyzing Sentiment

How do you guess whether a person felt positively or negatively about an experience, just from a short review they wrote?<p>In our second case study, analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).This task is an example of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification.</p>You will analyze the accuracy of your classifier, implement an actual classifier in an iPython notebook, and take a first stab at a core piece of the intelligent application you will build and deploy in your capstone. ...
Reading
19 个视频 (总计 75 分钟), 3 个阅读材料, 2 个测验
Video19 个视频
What is an intelligent restaurant review system?4分钟
Examples of classification tasks4分钟
Linear classifiers5分钟
Decision boundaries3分钟
Training and evaluating a classifier4分钟
What's a good accuracy?3分钟
False positives, false negatives, and confusion matrices6分钟
Learning curves5分钟
Class probabilities1分钟
Classification ML block diagram3分钟
Loading & exploring product review data2分钟
Creating the word count vector2分钟
Exploring the most popular product4分钟
Defining which reviews have positive or negative sentiment4分钟
Training a sentiment classifier3分钟
Evaluating a classifier & the ROC curve4分钟
Applying model to find most positive & negative reviews for a product4分钟
Exploring the most positive & negative aspects of a product4分钟
Reading3 个阅读材料
Slides presented in this module10分钟
Download the IPython Notebook used in this lesson to follow along10分钟
Reading: Analyzing product sentiment assignment10分钟
Quiz2 个练习
Classification14分钟
Analyzing product sentiment22分钟
4
完成时间(小时)
完成时间为 2 小时

Clustering and Similarity: Retrieving Documents

A reader is interested in a specific news article and you want to find a similar articles to recommend. What is the right notion of similarity? How do I automatically search over documents to find the one that is most similar? How do I quantitatively represent the documents in the first place?<p>In this third case study, retrieving documents, you will examine various document representations and an algorithm to retrieve the most similar subset. You will also consider structured representations of the documents that automatically group articles by similarity (e.g., document topic).</p>You will actually build an intelligent document retrieval system for Wikipedia entries in an iPython notebook....
Reading
17 个视频 (总计 76 分钟), 3 个阅读材料, 2 个测验
Video17 个视频
What is the document retrieval task?1分钟
Word count representation for measuring similarity6分钟
Prioritizing important words with tf-idf3分钟
Calculating tf-idf vectors5分钟
Retrieving similar documents using nearest neighbor search2分钟
Clustering documents task overview2分钟
Clustering documents: An unsupervised learning task4分钟
k-means: A clustering algorithm3分钟
Other examples of clustering6分钟
Clustering and similarity ML block diagram7分钟
Loading & exploring Wikipedia data5分钟
Exploring word counts5分钟
Computing & exploring TF-IDFs7分钟
Computing distances between Wikipedia articles5分钟
Building & exploring a nearest neighbors model for Wikipedia articles3分钟
Examples of document retrieval in action4分钟
Reading3 个阅读材料
Slides presented in this module10分钟
Download the IPython Notebook used in this lesson to follow along10分钟
Reading: Retrieving Wikipedia articles assignment10分钟
Quiz2 个练习
Clustering and Similarity12分钟
Retrieving Wikipedia articles18分钟
4.6
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职业方向

31%

完成这些课程后已开始新的职业生涯
工作福利

83%

通过此课程获得实实在在的工作福利

热门审阅

创建者 BLOct 17th 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

创建者 DSSep 28th 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

讲师

Avatar

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics

关于 University of 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....

关于 Machine Learning 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....
Machine Learning

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