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

4.6
stars
13,531 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

RM

Feb 2, 2022

I was very disappointed with the exclusion of the final courses and the capstone project. The most interesting part of specialization no longer exists and no plausible justification has been given.

RH

Jun 8, 2017

I felt this course did a good job introducing the student to Machine Learning. The examples and hands on assignments brought the concepts home. I was able to use the knowledge immediately at work.

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1926 - 1950 of 3,157 Reviews for Machine Learning Foundations: A Case Study Approach

By Carlos A P

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Oct 15, 2015

Really great !

By ARYAN G

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May 21, 2023

decent enough

By vamsi k k

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Jan 27, 2022

great vision!

By Hendro U

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Sep 27, 2020

HENDRO MANTAP

By KONETI S

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Jun 19, 2020

its very good

By Dr. R R N

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May 12, 2020

Great classes

By Rahul R

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Apr 23, 2020

More pratical

By Bala

•

Jun 7, 2019

Nice Teachers

By Xue

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Dec 2, 2018

Great course!

By WEI Y

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Jul 5, 2018

Great course!

By PiKaChu

•

Nov 26, 2017

good learning

By hari p b

•

Sep 18, 2017

Great course.

By Vitalie D

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Jun 27, 2017

Great course!

By 오재욱

•

Jan 17, 2017

great it was

By Runzhe C

•

Jan 2, 2017

Great Course!

By Nicholas S

•

Oct 7, 2016

Great course.

By Anaís G

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Apr 22, 2016

great course!

By NAMAN J

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Apr 20, 2016

Good Content!

By Mohammad H

•

Jan 20, 2016

great course!

By purushothaman r

•

Oct 4, 2015

Great course.

By Бек М

•

Oct 30, 2024

Очень хорошо

By DOSHI, U

•

Jul 13, 2023

Great Course

By Sankhadip J

•

Aug 30, 2020

Good subject

By Khushbu S

•

Aug 6, 2020

It was Nice.

By sathish v

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Jul 22, 2020

Wonderful ❣️