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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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1951 - 1975 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By SPENCER P

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Jul 5, 2020

Great Course

By Tarun B A

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Jul 2, 2020

Great Course

By Manikant R

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Jun 17, 2020

Great course

By JOYDIP M

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May 18, 2020

Very Helpful

By Rodrigo P d S

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Jan 18, 2020

Excelente!!!

By Muhammad Z H

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Aug 14, 2019

Learned alot

By Germanno T

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Nov 20, 2017

Great course

By Soumil D

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Nov 14, 2017

Loving this.

By Dan S

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Dec 11, 2016

Great class!

By sumita m

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Nov 5, 2016

great course

By Ghassan F

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Oct 11, 2016

Great course

By Luis D S

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Jun 4, 2016

Great Course

By clara c

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May 13, 2016

Great intro!

By Chenxi W

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Mar 21, 2016

Really good.

By Hans A

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Jan 31, 2016

Great course

By Ravshan

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Jan 22, 2016

Good Course!

By rajesh t

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Jan 13, 2016

Nice course!

By Tianyuan L

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Dec 29, 2015

Great course

By Josh M

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Dec 23, 2015

Great class!

By Trevor S

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Dec 6, 2015

Great format

By Liusicong

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Oct 12, 2015

Very useful!

By Elamaran S

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Nov 11, 2024

good course

By Harshdeep S

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Mar 21, 2022

Nice course

By Dwij J

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Jan 10, 2022

Good course

By Aqsa Z

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Jan 9, 2021

Nice course