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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

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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1876 - 1900 of 3,144 Reviews for Machine Learning Foundations: A Case Study Approach

By Josue S

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Sep 17, 2017

Excellent course

By Megh P

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Jun 8, 2017

very good course

By Thiago B

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Jun 7, 2017

Excelent course!

By Pappy S

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Apr 12, 2017

Excellent course

By veneshkumar g

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Apr 1, 2017

excellent course

By Dawit H

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Mar 2, 2017

Amazing lecture!

By Mars W

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Jan 23, 2017

very good course

By 丁俊南

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May 12, 2016

very good course

By Alexander S

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Feb 7, 2016

great and funny.

By Punita P

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Jan 22, 2021

it is very good

By KURALOVIYA N

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Oct 20, 2020

good experience

By Andrea M E G

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Aug 7, 2020

Excelent course

By DIVYANSH K

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Aug 6, 2020

course was good

By Sunanda G

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Jul 27, 2020

Great! Awesome!

By Pranav

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May 21, 2020

truly amazing!!

By Muhammad H S

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Aug 7, 2019

good experience

By Chris F

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Jan 11, 2017

Great course!!!

By ROHIT

•

Dec 21, 2016

Nice Experience

By Sridhar C

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Oct 22, 2016

Really good one

By 欧阳登

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Sep 25, 2016

非常不错的课程，适合初学者入门

By Tamir Z

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Mar 7, 2016

JUST AWESOME :D

By Nitin S

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Feb 7, 2016

Awesome course!

By Greg B

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Feb 6, 2016

Great overview!

By Pavan K K

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Feb 1, 2022

It was amazing

By Abdul B

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Sep 4, 2021

Good lectures