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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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1751 - 1775 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Alireza R

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May 29, 2017

The best instructor ever

By Rodolfo S

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May 8, 2016

Great course! Thank you.

By yongjin h

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Mar 5, 2016

觉得dato很好用，加上老师讲得非常好，good

By Yang G

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Nov 30, 2015

good, have learned a lot

By SHARMISTHA G

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May 4, 2023

good informative

course

By Gaurav K

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Sep 10, 2020

very good course to do.

By Muhammad T

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Jul 20, 2020

bATTER WAY FOR BEGINEER

By Dr. N P M

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May 26, 2020

Very informative course

By Danish H K

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Sep 5, 2017

Awesome Learning course

By ASHISH D

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May 28, 2017

A must for ML aspirants

By Haoyu J

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Apr 25, 2017

Good introduction to ML

By Túlio C

•

Jan 23, 2017

Enthusiastic professor.

By YongKwan K

•

Jan 11, 2016

This couser is awesome.

By Saeed U R

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Oct 4, 2020

Remarkable experience.

By Mrs.M.Amal M C T

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Sep 10, 2020

GOOD COURSE,GOOD CLASS

By KARTHICK G

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Aug 30, 2020

It's a best I think so

By Prabhakar A

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Aug 15, 2020

A wonderful experience

By Kedar P

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May 3, 2020

good foundation course

By ANKUR S

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Mar 20, 2020

best one for beginners

By Afaque A

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Aug 20, 2017

Excellent Explanation.

By Shivam A

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May 10, 2017

Amazing for beginners.

By Chandima

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Dec 26, 2016

Brilliant introduction

By Chao P

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Oct 11, 2016

impressive course!!!!!

By Arif A

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May 18, 2016

Very practical course.

By Daniele R

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Dec 14, 2015

molto bravi!

very good!