Machine Learning Specialization

开始时间: 10/13/2018 持续时间: Unknown

所在平台: Coursera专项课程

课程类别: 计算机科学

大学或机构: CourseraNew



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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.


4 courses

Machine Learning Foundations: A Case Study Approach

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 classific

Machine Learning: Regression

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is jus

Machine Learning: Classification

Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,.

Machine Learning: Clustering & Retrieval

Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each


Deep Learning Specialization on Coursera


华盛顿大学机器学习专项课程系列(Machine Learning Specialization),这个系列课程包含4门子课程,分别是 机器学习基础:案例研究 , 机器学习:回归 , 机器学习:分类, 机器学习:聚类与检索,感兴趣的同学可以关注: Build Intelligent Applications-Master machine learning fundamentals in four hands-on courses.


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