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Back to Machine Learning Foundations: A Case Study Approach

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

4.6
stars
13,529 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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1976 - 2000 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Aqsa Z

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Jan 9, 2021

Nice course

By SHYAM P J

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Nov 13, 2020

Excellent!!

By KIM B C

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Nov 13, 2020

excellent!!

By Abdul Q

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Oct 22, 2020

good course

By lavanya v

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Sep 28, 2020

very useful

By Nora T

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Sep 16, 2020

Very Useful

By Raditya Y A

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Sep 14, 2020

Thank You !

By Saikamal n

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Aug 26, 2020

outstanding

By Nithesha K

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Aug 24, 2020

Good course

By Steve W

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Aug 16, 2020

best course

By Utsab R

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Aug 11, 2020

Good Course

By Anitha T

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Jun 16, 2020

Nice Course

By Aayush R

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May 24, 2020

Good Course

By Rohith K

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May 20, 2020

very useful

By LEI L

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Mar 14, 2020

nice course

By Deepak G

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Sep 18, 2019

Good Course

By Pulkit S

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Aug 31, 2019

Good course

By Ankit V

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May 2, 2019

nice course

By Mao M

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Apr 29, 2019

I like it!!

By Anjali S D

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Oct 28, 2018

best course

By Ganesh P

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Oct 15, 2018

Very good f

By Basha S

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Sep 5, 2018

Excellent!!

By Nihal F

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Apr 9, 2018

Exceptional

By Tony G

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Sep 18, 2017

Really cool

By 韮滉步 鞚�

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Sep 14, 2017

very good !