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Machine Learning

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.

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What's inside

Four courses

Machine Learning Foundations: A Case Study Approach

(0 hours)

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? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

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Machine Learning: Regression

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Case Study - Predicting Housing Prices In this course, you will explore regularized linear regression models for predicting a continuous value (price) from input features. You will learn to handle large datasets, select between models, and analyze the impact of data aspects on predictions. By the end, you will be able to build a regression model to predict prices using a housing dataset and implement these techniques in Python.

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Machine Learning: Classification

(0 hours)

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,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

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Machine Learning: Clustering & Retrieval

(0 hours)

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 time you want to retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

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Time

125 hours

Level

Intermediate

Via

Coursera

Institution

University of Washington

Instructors

Carlos Guestrin

Emily Fox

Level

Intermediate

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