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

4.6
stars
13,494 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

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.

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

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1676 - 1700 of 3,144 Reviews for Machine Learning Foundations: A Case Study Approach

By Vinh N X

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Oct 8, 2021

i feel the course is so good

By ADARSH K D I S

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Jan 4, 2021

too much helpfull ..thanks:)

By PAVITHRA B

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Sep 29, 2020

Excellent Fundamental course

By Kranthi K G

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Sep 15, 2020

Perfect course for beginners

By Hemanth

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Jul 8, 2020

very helpful and informative

By Soumya M

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Jun 28, 2020

Really helpful for my career

By Rajyavardhan S R

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Jun 29, 2019

Amazing Course, learnt a lot

By Aakash D S

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May 13, 2019

Simply Awesome, Thanks a Lot

By Ian M C

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Nov 26, 2018

It was a great introduction!

By Charlie C H

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Jun 10, 2018

Very useful and clear lesson

By Giuseppe S

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Jan 26, 2018

Nice course ! I enjoyed it !

By Liekens W

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Dec 16, 2016

Great course for starting ML

By Prashanth P P

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Nov 26, 2016

Loved it. Simple and crisp !

By 尧浩华

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Sep 30, 2015

It is very helpful my study!

By Yash S

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Apr 6, 2021

awsome course for beginners

By Andres F R V

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Nov 18, 2020

very good,,,, i am happy. i

By Anji B P

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Jun 6, 2019

Good Course with case study

By Chen G

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Feb 27, 2017

A great introductory to ML.

By Awantik D

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Feb 10, 2017

Perfect for getting started

By Sam C

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Oct 23, 2016

interesting to follow alone

By Oleksii R

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Jun 4, 2016

Great course. Thanks a lot.

By Vijai K S

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Dec 6, 2015

So far it has been awesome.

By Mustapha B

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Jul 19, 2022

Excellent !! thank you !!

By Merve E U

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Dec 13, 2020

Thank you for your support

By Ben R

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Oct 17, 2020

great course very useful!!