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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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1901 - 1925 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By ROHIT

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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 24, 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 3, 2021

Good lectures

By NITHIN P

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May 19, 2021

It helped lot.

By Ruwanthi M

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Nov 27, 2020

A best course.

By Md R I

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Nov 11, 2020

Awesome course

By PRATHEEKSHA. P K

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Oct 26, 2020

Nice. Loved it

By K. j S

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Aug 22, 2020

well experence

By Hewavitharanage S A G

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Jul 25, 2020

awesome course

By Mr. S P K

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Apr 17, 2020

Simply Awesome

By Odai M

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Sep 20, 2019

Extremely fun.

By byeongwook.seo

•

Oct 27, 2017

Great Lecture!

By surajit d

•

Aug 20, 2016

Awesome course

By Bilal A S

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Jun 6, 2016

Very useful ,,

By Andrew M

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Mar 29, 2016

Super awesome.

By Van Q

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Mar 1, 2016

very practical

By Lee S H

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Jan 25, 2016

it's very fun

By Viet N

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Nov 25, 2015

Awesome course

By kardoworld

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Oct 28, 2015

Awesome Course

By Carlos A P

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Oct 15, 2015

Really great !