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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 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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2001 - 2025 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By 王曾

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Sep 10, 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 9, 2016

Outstanding

By Chengjun J

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Feb 8, 2016

good start!

By Thuong D H

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Jan 13, 2016

Good course

By Zhalgas N (

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Nov 6, 2024

Все хорошо

By Vaibhav K

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Sep 19, 2022

Excellent

By Riya G

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Jul 13, 2022

good couse

By 蔡孟蓁

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Apr 1, 2021

有難度但可以學到很多

By ROBAD M

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Nov 26, 2020

excellence

By Bhavesh J

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Nov 15, 2020

Fantastic!

By Durodola A D

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Sep 1, 2020

excellent!

By Rohit R D

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Aug 16, 2020

Regression

By LUIS A M Y

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Jul 26, 2020

Excelent!!

By Ifzal A M

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May 19, 2020

excellent.

By Anunathan G S

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Aug 5, 2018

Lucid over

By Nancy L

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Apr 27, 2018

Thank you!

By carloswhite

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Mar 17, 2018

it is nice

By zulfikar A

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Dec 30, 2017

Best .....

By 康佳星

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Sep 13, 2017

入门基础的成就感不错

By 王泽元

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May 14, 2017

meaningful

By Júlio T

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Mar 25, 2017

Very Good!

By Frank

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Nov 1, 2016

实践与理论的完美结合

By Rogerio B

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May 14, 2016

Excellent!

By Andrey B

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Apr 9, 2016

Thank you!