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

4.6
stars
13,499 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

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.

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

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1976 - 2000 of 3,146 Reviews for Machine Learning Foundations: A Case Study Approach

By Steve W

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Aug 16, 2020

best course

By Utsab R

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Aug 10, 2020

Good Course

By Anitha T

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Jun 15, 2020

Nice Course

By Aayush R

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May 24, 2020

Good Course

By Rohith K

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May 19, 2020

very useful

By LEI L

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Mar 13, 2020

nice course

By Deepak G

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Sep 18, 2019

Good Course

By Pulkit S

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Aug 30, 2019

Good course

By Ankit V

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May 1, 2019

nice course

By MAO M

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Apr 28, 2019

I like it!!

By Anjali S D

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Oct 27, 2018

best course

By Ganesh P

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Oct 14, 2018

Very good f

By Basha S

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Sep 4, 2018

Excellent!!

By Nihal F

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Apr 9, 2018

Exceptional

By Tony G

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Sep 17, 2017

Really cool

By 태경 이

•

Sep 14, 2017

very good !

By 王曾

•

Sep 9, 2017

good course

By Weilin C

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Aug 26, 2017

very detail

By Néstor R E P

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Feb 8, 2016

Outstanding

By Chengjun J

•

Feb 8, 2016

good start!

By Thuong D H

•

Jan 13, 2016

Good course

By Zhalgas N (

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Nov 5, 2024

Все хорошо

By Vaibhav K

•

Sep 19, 2022

Excellent

By Riya G

•

Jul 13, 2022

good couse

By 蔡孟蓁

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Mar 31, 2021

有難度但可以學到很多