课程信息
4.6
7,683 个评分
1,897 个审阅
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....
Stacks

Course 1 of 4 in the

Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Clock

Approx. 22 hours to complete

建议:6 weeks of study, 5-8 hours/week...
Comment Dots

English

字幕:English, Korean, Vietnamese, Chinese (Simplified)...

您将获得的技能

Python ProgrammingMachine Learning ConceptsMachine LearningDeep Learning
Stacks

Course 1 of 4 in the

Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Clock

Approx. 22 hours to complete

建议:6 weeks of study, 5-8 hours/week...
Comment Dots

English

字幕:English, Korean, Vietnamese, Chinese (Simplified)...

教学大纲 - 您将从这门课程中学到什么

Week
1
Clock
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(共 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
Week
2
Clock
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(共 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
Week
3
Clock
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(共 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
Week
4
Clock
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(共 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
Direction Signs

31%

完成这些课程后已开始新的职业生涯
Briefcase

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 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!

讲师

Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

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

常见问题

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

还有其他问题吗?请访问 学生帮助中心