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1,916 個審閱
專項課程

第 1 門課程(共 4 門),位於

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完成時間(小時)

完成時間大約為22 小時

建議:6 weeks of study, 5-8 hours/week...
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字幕:英語(English), 韓語, 越南語, 中文(簡體)...

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Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning
專項課程

第 1 門課程(共 4 門),位於

100% online

100% online

立即開始,按照自己的計劃學習。
可靈活調整截止日期

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(小時)

完成時間大約為22 小時

建議:6 weeks of study, 5-8 hours/week...
可選語言

英語(English)

字幕:英語(English), 韓語, 越南語, 中文(簡體)...

教學大綱 - 您將從這門課程中學到什麼

1
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 84 分鐘), 6 個閱讀材料
Video18 個視頻
Who we are5分鐘
Machine learning is changing the world3分鐘
Why a case study approach?7分鐘
Specialization overview6分鐘
How we got into ML3分鐘
Who is this specialization for?4分鐘
What you'll be able to do分鐘
The capstone and an example intelligent application6分鐘
The future of intelligent applications2分鐘
Starting an IPython Notebook5分鐘
Creating variables in Python7分鐘
Conditional statements and loops in Python8分鐘
Creating functions and lambdas in Python3分鐘
Starting GraphLab Create & loading an SFrame4分鐘
Canvas for data visualization4分鐘
Interacting with columns of an SFrame4分鐘
Using .apply() for data transformation5分鐘
Reading6 個閱讀材料
Important Update regarding the Machine Learning Specialization10分鐘
Slides presented in this module10分鐘
Reading: Getting started with Python, IPython Notebook & GraphLab Create10分鐘
Reading: where should my files go?10分鐘
Download the IPython Notebook used in this lesson to follow along10分鐘
Download the IPython Notebook used in this lesson to follow along10分鐘
2
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 82 分鐘), 3 個閱讀材料, 2 個測驗
Video19 個視頻
What is the goal and how might you naively address it?3分鐘
Linear Regression: A Model-Based Approach5分鐘
Adding higher order effects4分鐘
Evaluating overfitting via training/test split6分鐘
Training/test curves4分鐘
Adding other features2分鐘
Other regression examples3分鐘
Regression ML block diagram5分鐘
Loading & exploring house sale data7分鐘
Splitting the data into training and test sets2分鐘
Learning a simple regression model to predict house prices from house size3分鐘
Evaluating error (RMSE) of the simple model2分鐘
Visualizing predictions of simple model with Matplotlib4分鐘
Inspecting the model coefficients learned1分鐘
Exploring other features of the data6分鐘
Learning a model to predict house prices from more features3分鐘
Applying learned models to predict price of an average house5分鐘
Applying learned models to predict price of two fancy houses7分鐘
Reading3 個閱讀材料
Slides presented in this module10分鐘
Download the IPython Notebook used in this lesson to follow along10分鐘
Reading: Predicting house prices assignment10分鐘
Quiz2 個練習
Regression18分鐘
Predicting house prices6分鐘
3
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 75 分鐘), 3 個閱讀材料, 2 個測驗
Video19 個視頻
What is an intelligent restaurant review system?4分鐘
Examples of classification tasks4分鐘
Linear classifiers5分鐘
Decision boundaries3分鐘
Training and evaluating a classifier4分鐘
What's a good accuracy?3分鐘
False positives, false negatives, and confusion matrices6分鐘
Learning curves5分鐘
Class probabilities1分鐘
Classification ML block diagram3分鐘
Loading & exploring product review data2分鐘
Creating the word count vector2分鐘
Exploring the most popular product4分鐘
Defining which reviews have positive or negative sentiment4分鐘
Training a sentiment classifier3分鐘
Evaluating a classifier & the ROC curve4分鐘
Applying model to find most positive & negative reviews for a product4分鐘
Exploring the most positive & negative aspects of a product4分鐘
Reading3 個閱讀材料
Slides presented in this module10分鐘
Download the IPython Notebook used in this lesson to follow along10分鐘
Reading: Analyzing product sentiment assignment10分鐘
Quiz2 個練習
Classification14分鐘
Analyzing product sentiment22分鐘
4
完成時間(小時)
完成時間為 2 小時

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 個視頻(共 76 分鐘), 3 個閱讀材料, 2 個測驗
Video17 個視頻
What is the document retrieval task?1分鐘
Word count representation for measuring similarity6分鐘
Prioritizing important words with tf-idf3分鐘
Calculating tf-idf vectors5分鐘
Retrieving similar documents using nearest neighbor search2分鐘
Clustering documents task overview2分鐘
Clustering documents: An unsupervised learning task4分鐘
k-means: A clustering algorithm3分鐘
Other examples of clustering6分鐘
Clustering and similarity ML block diagram7分鐘
Loading & exploring Wikipedia data5分鐘
Exploring word counts5分鐘
Computing & exploring TF-IDFs7分鐘
Computing distances between Wikipedia articles5分鐘
Building & exploring a nearest neighbors model for Wikipedia articles3分鐘
Examples of document retrieval in action4分鐘
Reading3 個閱讀材料
Slides presented in this module10分鐘
Download the IPython Notebook used in this lesson to follow along10分鐘
Reading: Retrieving Wikipedia articles assignment10分鐘
Quiz2 個練習
Clustering and Similarity12分鐘
Retrieving Wikipedia articles18分鐘
4.6
職業方向

31%

完成這些課程後已開始新的職業生涯
工作福利

83%

通過此課程獲得實實在在的工作福利

熱門審閱

創建者 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

創建者 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!

講師

Avatar

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 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....

關於 Machine Learning 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....
Machine Learning

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