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自分のスケジュールですぐに学習を始めてください。

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約24時間で修了

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

英語

字幕:英語, 韓国語, ベトナム語, 中国語(簡体)

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Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning

次における4の1コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

約24時間で修了

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

英語

字幕:英語, 韓国語, ベトナム語, 中国語(簡体)

シラバス - 本コースの学習内容

1
2時間で修了

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件のビデオ (合計84分), 6 readings
18件のビデオ
Who we are5 分
Machine learning is changing the world3 分
Why a case study approach?7 分
Specialization overview6 分
How we got into ML3 分
Who is this specialization for?4 分
What you'll be able to do57
The capstone and an example intelligent application6 分
The future of intelligent applications2 分
Starting an IPython Notebook5 分
Creating variables in Python7 分
Conditional statements and loops in Python8 分
Creating functions and lambdas in Python3 分
Starting GraphLab Create & loading an SFrame4 分
Canvas for data visualization4 分
Interacting with columns of an SFrame4 分
Using .apply() for data transformation5 分
6件の学習用教材
Important Update regarding the Machine Learning Specialization10 分
Slides presented in this module10 分
Reading: Getting started with Python, IPython Notebook & GraphLab Create10 分
Reading: where should my files go?10 分
Download the IPython Notebook used in this lesson to follow along10 分
Download the IPython Notebook used in this lesson to follow along10 分
2
2時間で修了

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件のビデオ (合計82分), 3 readings, 2 quizzes
19件のビデオ
What is the goal and how might you naively address it?3 分
Linear Regression: A Model-Based Approach5 分
Adding higher order effects4 分
Evaluating overfitting via training/test split6 分
Training/test curves4 分
Adding other features2 分
Other regression examples3 分
Regression ML block diagram5 分
Loading & exploring house sale data7 分
Splitting the data into training and test sets2 分
Learning a simple regression model to predict house prices from house size3 分
Evaluating error (RMSE) of the simple model2 分
Visualizing predictions of simple model with Matplotlib4 分
Inspecting the model coefficients learned1 分
Exploring other features of the data6 分
Learning a model to predict house prices from more features3 分
Applying learned models to predict price of an average house5 分
Applying learned models to predict price of two fancy houses7 分
3件の学習用教材
Slides presented in this module10 分
Download the IPython Notebook used in this lesson to follow along10 分
Reading: Predicting house prices assignment10 分
2の練習問題
Regression18 分
Predicting house prices6 分
3
2時間で修了

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件のビデオ (合計75分), 3 readings, 2 quizzes
19件のビデオ
What is an intelligent restaurant review system?4 分
Examples of classification tasks4 分
Linear classifiers5 分
Decision boundaries3 分
Training and evaluating a classifier4 分
What's a good accuracy?3 分
False positives, false negatives, and confusion matrices6 分
Learning curves5 分
Class probabilities1 分
Classification ML block diagram3 分
Loading & exploring product review data2 分
Creating the word count vector2 分
Exploring the most popular product4 分
Defining which reviews have positive or negative sentiment4 分
Training a sentiment classifier3 分
Evaluating a classifier & the ROC curve4 分
Applying model to find most positive & negative reviews for a product4 分
Exploring the most positive & negative aspects of a product4 分
3件の学習用教材
Slides presented in this module10 分
Download the IPython Notebook used in this lesson to follow along10 分
Reading: Analyzing product sentiment assignment10 分
2の練習問題
Classification14 分
Analyzing product sentiment22 分
4
2時間で修了

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件のビデオ (合計76分), 3 readings, 2 quizzes
17件のビデオ
What is the document retrieval task?1 分
Word count representation for measuring similarity6 分
Prioritizing important words with tf-idf3 分
Calculating tf-idf vectors5 分
Retrieving similar documents using nearest neighbor search2 分
Clustering documents task overview2 分
Clustering documents: An unsupervised learning task4 分
k-means: A clustering algorithm3 分
Other examples of clustering6 分
Clustering and similarity ML block diagram7 分
Loading & exploring Wikipedia data5 分
Exploring word counts5 分
Computing & exploring TF-IDFs7 分
Computing distances between Wikipedia articles5 分
Building & exploring a nearest neighbors model for Wikipedia articles3 分
Examples of document retrieval in action4 分
3件の学習用教材
Slides presented in this module10 分
Download the IPython Notebook used in this lesson to follow along10 分
Reading: Retrieving Wikipedia articles assignment10 分
2の練習問題
Clustering and Similarity12 分
Retrieving Wikipedia articles18 分
4.6
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人気のレビュー

by 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

by 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!

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

ワシントン大学(University of Washington)について

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....

機械学習の専門講座について

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....
機械学習

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