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

4.6
stars
13,485 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

PM

Aug 18, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

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.

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2826 - 2850 of 3,140 Reviews for Machine Learning Foundations: A Case Study Approach

By Binil K

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Jan 10, 2016

Really great one!!

By Hiếu N Q

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Dec 28, 2015

Good for ML newbie

By amit d

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Feb 3, 2020

nice explaination

By Arnab N

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Jan 5, 2020

Very nice program

By Rahul S

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Dec 19, 2020

GREAT EXPERIANCE

By SURUTHI T

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Jul 5, 2020

more informative

By Oscar M

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May 29, 2016

Very insightfull

By Tulasi P D

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Jul 15, 2020

it is so useful

By Rohit K

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Apr 17, 2020

very intersting

By shane

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Oct 22, 2015

Very practical.

By Rohit K S

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Sep 30, 2020

Good Course!!

By Divyashree

•

Sep 14, 2020

A good course

By SHAHID S

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May 16, 2022

Nice Content

By ANURAG Y

•

Nov 30, 2021

good teacher

By Rupali G

•

Nov 2, 2017

good content

By Andre G

•

May 14, 2016

Good course.

By 廖敏宏

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Sep 24, 2020

Very useful

By P.BHUVANASHREE

•

Sep 18, 2020

interesting

By HASNA V N

•

Jul 19, 2020

Good course

By Shubham D

•

Dec 3, 2016

nice course

By Le H P

•

Aug 16, 2019

well done!

By Daniel Ø

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Jan 18, 2016

very basic

By Muhammad A K

•

Nov 27, 2020

very good

By Sayam N

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Sep 25, 2020

Excellent

By Aishwarya S

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Jul 5, 2020

very nice