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

4.6
stars
13,494 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 17, 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 20, 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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1826 - 1850 of 3,144 Reviews for Machine Learning Foundations: A Case Study Approach

By Nancy Q

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Feb 13, 2017

highly recommended

By Adrian B

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Sep 14, 2016

Very recommendable

By Chen Y

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Feb 18, 2016

It's a neat course

By RISHABH T

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Feb 11, 2016

Excellent Course .

By Vlad G

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Jan 29, 2016

amazing experience

By DEVINENI T

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Oct 7, 2022

its very helpful

By Fevzi E K

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Oct 11, 2020

gerçekten çok iyi

By SHARUKH A

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Jul 18, 2020

great course.....

By Bhavya D J

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Jun 15, 2020

amazing course...

By Abhay S

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Jun 14, 2020

i love course era

By Ridwanul H T

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Apr 11, 2020

Excellent course.

By Shiwanshu K

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May 20, 2019

Beautiful course!

By Yaakov M

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Jun 17, 2017

Nice introduction

By Bum-Joo C

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Jun 2, 2017

Good! and Useful!

By Israel C

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May 22, 2017

Excellent Course!

By Ji H

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Jan 12, 2017

excellent course.

By Dheeraj A

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Jan 3, 2017

Excellent Course.

By Shan-Jyun W

•

Dec 31, 2016

Excellent course!

By gaozhipeng

•

Dec 27, 2016

NICE INTRODUCTION

By Saksham S

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Oct 8, 2016

Very good course.

By Mario A R M

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Sep 17, 2016

Excellent course!

By Liang Q

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Sep 13, 2016

good intro course

By Vincent L

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Aug 18, 2016

Excellent Course!

By Apurva A

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Mar 2, 2016

Excellent Course!

By Anthony

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Nov 1, 2015

fantastic teacher