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

4.6
stars
13,544 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

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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3026 - 3050 of 3,159 Reviews for Machine Learning Foundations: A Case Study Approach

By Aleksey C

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Dec 11, 2016

....mmm fsdfg gsgsd sgsdgsdg sdsdgsdg ggsgsd sgdsdgsg

By Christos M

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Feb 1, 2023

The assignments were really short and extremely easy

By HITESH D

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Jun 15, 2020

Installing software parts gave me a very hard time.

By Bastian M P

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Jun 1, 2016

Could go a little more in detail on the algorithms.

By Jaime O

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Jan 31, 2017

The Deep Learning part needs to be improved

By Chen S

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Oct 26, 2015

Very basic, the quizzes aren't clear enough

By Li-Pu C

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Oct 29, 2020

A little bit too easy, but good for rookie

By Harsh V K

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May 8, 2019

Should use Python 3 instead of Python 2

By Deleted A

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Apr 3, 2021

sofware guideline is quiet useless

By Yu G

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Feb 7, 2021

No idea what to write here...

By Jorge C

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May 29, 2016

It is a very simple course.

By Aditya A

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Apr 10, 2025

few quiz answers are wrong

By Ricardo S

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Aug 10, 2021

Feels a bit out dated

By RAGHUPATHI R R

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Jun 25, 2020

Good for knowledge

By Fredick A S

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Apr 6, 2018

No python..

By Nasimul J F

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Aug 16, 2020

THANK YOU.

By Kai C

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Nov 24, 2015

Too easy

By Geetha G

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Aug 15, 2021

good

By Anshu R

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Sep 12, 2020

good

By 18103048 H - S C

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Sep 4, 2020

Good

By MD. S K S

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Aug 22, 2020

cool

By tarun v s n

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Jul 23, 2020

Good

By Abhinav S

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May 10, 2020

good

By Bindra B

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Jun 20, 2021

k

By CHEE W M

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Sep 26, 2019

V