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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

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.

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.

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1776 - 1800 of 3,140 Reviews for Machine Learning Foundations: A Case Study Approach

By Mauro L

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Jan 19, 2017

Excellent Professors.

By Brahmeswara Y

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Mar 27, 2016

Good overview course.

By Kumar N

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Feb 14, 2016

Awesome for beginners

By Amine L

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Oct 25, 2015

Great Course, thanks!

By Robin P

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Jun 4, 2021

very pleasant course

By KIRAN K

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Aug 27, 2020

excellent experience

By ANMOL B 1

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Jun 3, 2020

Great way to explain

By PARK J

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Oct 28, 2019

very easy to access,

By Chandan K M

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Feb 27, 2019

well crafted course.

By Brian N

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May 19, 2018

Its very refreshing.

By Diego S L

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May 13, 2018

It's a great course!

By stephon_lu

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Nov 8, 2017

the course is great!

By Sarah N

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Apr 23, 2017

Great Intro into ML.

By Yongrui H

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Mar 14, 2017

This course is good.

By Irina L

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Jan 29, 2017

Vary good course!!!!

By Sumit S

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Sep 3, 2016

Best hands on course

By Lucas T

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Dec 6, 2015

Really great course!

By Vahid R

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Apr 12, 2022

I realy enjoyed it.

By Sankul P

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Sep 14, 2021

excellent thank you

By Antonios M

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Feb 10, 2021

It's a great course

By Raj K

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Dec 15, 2020

Very knowledgeable.

By Neeti M

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Nov 1, 2020

good.. i like it...

By TEJAL H

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Jul 30, 2020

Fabulous instructor

By MALLAIAH V

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Jun 12, 2020

it is very helpfull

By Dimaz A P

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May 23, 2020

Best course so far!