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

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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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By Veceslav K

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Dec 29, 2015

Super fun!

By Andres F Z M

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Sep 26, 2024

Very good

By Jorge L U V

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Oct 3, 2023

Excelente

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Apr 12, 2023

very good

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Dec 31, 2022

very well

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Jul 22, 2022

excellent

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Apr 12, 2022

excellent

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Dec 14, 2021

excellent

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Nov 27, 2021

excellent

By Sri P N R L C S

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Nov 24, 2021

Thank you

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Aug 12, 2021

Excellent

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Apr 18, 2021

excellent

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Mar 12, 2021

Excellent

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Dec 12, 2020

Excellent

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Nov 30, 2020

excellent

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Nov 28, 2020

very nice

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Oct 15, 2020

very nice

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Sep 29, 2020

Excellent

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Sep 25, 2020

EXCELLENT

By Satagonda J

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Sep 22, 2020

Excellent

By Rahul D

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Sep 21, 2020

wonderful