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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 16, 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 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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1851 - 1875 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Shan-Jyun W

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Dec 31, 2016

Excellent course!

By gaozhipeng

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Dec 26, 2016

NICE INTRODUCTION

By Saksham S

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Oct 8, 2016

Very good course.

By Mario A R M

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Sep 16, 2016

Excellent course!

By Liang Q

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Sep 12, 2016

good intro course

By Vincent L

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Aug 18, 2016

Excellent Course!

By Apurva A

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Mar 2, 2016

Excellent Course!

By Anthony

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Nov 1, 2015

fantastic teacher

By Nar N

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Oct 30, 2024

Very Good Course

By Sunil k

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Jul 22, 2022

very good course

By Amlan D

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May 4, 2022

Excellent Course

By SUDABATHULA J P

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Dec 5, 2021

excellent course

By Melisha Q

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Jul 4, 2021

very good course

By Kalpana j

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May 6, 2021

excellent course

By Jubair A

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Apr 6, 2021

Very nice course

By Thangaraju, S

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Mar 7, 2021

Excellent course

By SUGUNA M

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Nov 18, 2020

excellent course

By ANUJ S

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Sep 28, 2020

Excellent Course

By VIGNESH K

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Sep 4, 2020

Send certificate

By Kumar S

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Sep 1, 2020

great experience

By Rushi B

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Jul 25, 2020

excellent course

By DHARMESHWARAN S

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Jul 13, 2020

excellent course

By Rohit

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Jul 9, 2020

Excellent course

By SAMRATSAKHA

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Jul 8, 2020

Very nice course

By Syed T A

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Jun 23, 2020

very interesting