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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
13,509 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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1926 - 1950 of 3,150 Reviews for Machine Learning Foundations: A Case Study Approach

By Xue

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Dec 3, 2018

Great course!

By WEI Y

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Jul 6, 2018

Great course!

By PiKaChu

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Nov 27, 2017

good learning

By hari p b

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Sep 19, 2017

Great course.

By Vitalie D

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Jun 28, 2017

Great course!

By 鞓れ灛鞖�

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Jan 17, 2017

great it was

By Runzhe C

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Jan 3, 2017

Great Course!

By Nicholas S

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Oct 8, 2016

Great course.

By Ana铆s G

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Apr 22, 2016

great course!

By NAMAN J

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Apr 21, 2016

Good Content!

By Mohammad H

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Jan 21, 2016

great course!

By purushothaman r

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Oct 4, 2015

Great course.

By 袘械泻 袦

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Oct 30, 2024

袨褔械薪褜 褏芯褉芯褕芯

By DOSHI, U

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Jul 14, 2023

Great Course

By Sankhadip J

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Aug 30, 2020

Good subject

By Khushbu S

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Aug 7, 2020

It was Nice.

By sathish v

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Jul 23, 2020

Wonderful 鉂ｏ笍

By Sahil L

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Jul 13, 2020

Great Course

By SPENCER P

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Jul 6, 2020

Great Course

By Tarun B A

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Jul 2, 2020

Great Course

By Manikant R

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Jun 18, 2020

Great course

By JOYDIP M

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May 19, 2020

Very Helpful

By Rodrigo P d S

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Jan 18, 2020

Excelente!!!

By Muhammad Z H

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Aug 14, 2019

Learned alot

By Germanno T

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Nov 20, 2017

Great course