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Learner Reviews & Feedback for Machine Learning Foundations: A Case Study Approach by University of Washington

4.6
stars
13,527 ratings

About the Course

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....

Top reviews

BL

Oct 16, 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

SZ

Dec 19, 2016

Great course! Emily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

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1651 - 1675 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Rodrigo T

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Sep 5, 2017

Excelent Course, nice examples.

By TaeKiJeong

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Jan 5, 2017

The best module, I ever studied

By Metodi K T

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Nov 19, 2016

This course was very fun to me.

By Eden C

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Mar 23, 2016

Fantastic! Love the professors.

By 汪彦龙

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Jan 19, 2016

Case study approach is awesome!

By Roy V

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Dec 28, 2015

Very nice overview of the field

By Tandranki M

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Jan 25, 2022

Very helpful to gain knowledge

By Esteban G L

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Mar 30, 2021

Good entry level course for ML

By Abdelrahman A

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Nov 19, 2020

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By Arshiya n

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Jul 19, 2020

Thank you so much it was super

By Bowen F

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May 6, 2020

grrrrrreat! I love this course

By zoom

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Mar 5, 2018

good ,It is very useful for me

By Miguel Z

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Jan 27, 2018

Excellent. Highly recommended.

By Ashok B

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Jul 23, 2017

Excellent coverage of the conc

By ketan k

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Nov 7, 2016

Very good introduction course.

By David L

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Sep 23, 2016

Very up to date and practical.

By Thalles R

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May 3, 2016

Amazing!

Best M.L. course ever.

By Ranu

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Feb 23, 2016

Looking forward to learn more.

By CynthiaEast

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Dec 19, 2015

A good course to introduction.

By Kurtis H

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Nov 5, 2015

The instructors are excellent.

By Abdul-Rasheed O O

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May 19, 2022

Easy to follow and understand

By Juan C C G

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Nov 3, 2020

Es buen curso de introducción

By Saurabh S

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Sep 22, 2020

Amazing course. Lot to Learn.

By SUJAY P

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Aug 20, 2020

great teaching .....thank u !

By QIANGQIANG G

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May 7, 2020

well-organized course, thanks