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다음 전문 분야의 4개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

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완료하는 데 약 24시간 필요

권장: 6 weeks of study, 5-8 hours/week...

영어

자막: 영어, 한국어, 베트남어, 중국어 (간체자)

귀하가 습득할 기술

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning

다음 전문 분야의 4개 강좌 중 1번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 24시간 필요

권장: 6 weeks of study, 5-8 hours/week...

영어

자막: 영어, 한국어, 베트남어, 중국어 (간체자)

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

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.

...
18 videos (Total 84 min), 6 readings
18개의 동영상
Who we are5m
Machine learning is changing the world3m
Why a case study approach?7m
Specialization overview6m
How we got into ML3m
Who is this specialization for?4m
What you'll be able to do57
The capstone and an example intelligent application6m
The future of intelligent applications2m
Starting an IPython Notebook5m
Creating variables in Python7m
Conditional statements and loops in Python8m
Creating functions and lambdas in Python3m
Starting GraphLab Create & loading an SFrame4m
Canvas for data visualization4m
Interacting with columns of an SFrame4m
Using .apply() for data transformation5m
6개의 읽기 자료
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Reading: Getting started with Python, IPython Notebook & GraphLab Create10m
Reading: where should my files go?10m
Download the IPython Notebook used in this lesson to follow along10m
Download the IPython Notebook used in this lesson to follow along10m
2
완료하는 데 2시간 필요

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.

...
19 videos (Total 82 min), 3 readings, 2 quizzes
19개의 동영상
What is the goal and how might you naively address it?3m
Linear Regression: A Model-Based Approach5m
Adding higher order effects4m
Evaluating overfitting via training/test split6m
Training/test curves4m
Adding other features2m
Other regression examples3m
Regression ML block diagram5m
Loading & exploring house sale data7m
Splitting the data into training and test sets2m
Learning a simple regression model to predict house prices from house size3m
Evaluating error (RMSE) of the simple model2m
Visualizing predictions of simple model with Matplotlib4m
Inspecting the model coefficients learned1m
Exploring other features of the data6m
Learning a model to predict house prices from more features3m
Applying learned models to predict price of an average house5m
Applying learned models to predict price of two fancy houses7m
3개의 읽기 자료
Slides presented in this module10m
Download the IPython Notebook used in this lesson to follow along10m
Reading: Predicting house prices assignment10m
2개 연습문제
Regression18m
Predicting house prices6m
3
완료하는 데 2시간 필요

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.

...
19 videos (Total 75 min), 3 readings, 2 quizzes
19개의 동영상
What is an intelligent restaurant review system?4m
Examples of classification tasks4m
Linear classifiers5m
Decision boundaries3m
Training and evaluating a classifier4m
What's a good accuracy?3m
False positives, false negatives, and confusion matrices6m
Learning curves5m
Class probabilities1m
Classification ML block diagram3m
Loading & exploring product review data2m
Creating the word count vector2m
Exploring the most popular product4m
Defining which reviews have positive or negative sentiment4m
Training a sentiment classifier3m
Evaluating a classifier & the ROC curve4m
Applying model to find most positive & negative reviews for a product4m
Exploring the most positive & negative aspects of a product4m
3개의 읽기 자료
Slides presented in this module10m
Download the IPython Notebook used in this lesson to follow along10m
Reading: Analyzing product sentiment assignment10m
2개 연습문제
Classification14m
Analyzing product sentiment22m
4
완료하는 데 2시간 필요

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.

...
17 videos (Total 76 min), 3 readings, 2 quizzes
17개의 동영상
What is the document retrieval task?1m
Word count representation for measuring similarity6m
Prioritizing important words with tf-idf3m
Calculating tf-idf vectors5m
Retrieving similar documents using nearest neighbor search2m
Clustering documents task overview2m
Clustering documents: An unsupervised learning task4m
k-means: A clustering algorithm3m
Other examples of clustering6m
Clustering and similarity ML block diagram7m
Loading & exploring Wikipedia data5m
Exploring word counts5m
Computing & exploring TF-IDFs7m
Computing distances between Wikipedia articles5m
Building & exploring a nearest neighbors model for Wikipedia articles3m
Examples of document retrieval in action4m
3개의 읽기 자료
Slides presented in this module10m
Download the IPython Notebook used in this lesson to follow along10m
Reading: Retrieving Wikipedia articles assignment10m
2개 연습문제
Clustering and Similarity12m
Retrieving Wikipedia articles18m
4.6
2064개의 리뷰Chevron Right

36%

이 강좌를 수료한 후 새로운 경력 시작하기

32%

이 강좌를 통해 확실한 경력상 이점 얻기

Machine Learning Foundations: A Case Study Approach의 최상위 리뷰

대학: 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

대학: DPFeb 15th 2016

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

강사

Avatar

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
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Emily Fox

Amazon Professor of Machine Learning
Statistics

워싱턴 대학교 정보

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....

기계 학습 전문 분야 정보

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....
기계 학습

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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