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

4.6
stars
13,376 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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1701 - 1725 of 3,116 Reviews for Machine Learning Foundations: A Case Study Approach

By Ahtasham H

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Feb 5, 2017

Amazing Learning approach.

By Socrates M

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Nov 20, 2016

It is really cool course .

By Rabish K

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Oct 21, 2016

Excellent. Very Intuitive.

By Danish R

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Oct 14, 2016

A good introductory course

By Ehsan T

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Oct 11, 2016

the best Course i ever see

By Vikash M

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Sep 13, 2016

Great course for starters!

By felix a f a

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Feb 21, 2016

Really Good Introduction!!

By Soumya R

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Feb 4, 2016

Great for beginners in ML!

By Deleted A

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Jan 6, 2016

Great intro to the ML area

By Fangyi Z

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Nov 19, 2015

This course is good for me

By Tommy W

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Oct 19, 2015

Very good!!! Super helpful

By 이원일

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Feb 22, 2022

머신러닝의 핵심적인 부분을 학습할 수 있었다.

By Yash J

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Jul 27, 2020

Excellent Teaching Method

By Salman T

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Jul 24, 2020

Thank you for teaching us

By Vishal K

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Feb 23, 2020

Very much hands on course

By Lan J

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Nov 2, 2018

Love it. Easy and useful.

By Saifullah

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Jun 7, 2018

Very well designed course

By Naga V

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Mar 30, 2018

great course for starters

By PengChienKai

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Aug 4, 2017

Very nice for learning ML

By Jiaqi Z

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Sep 25, 2016

very clear and practical!

By Omar A C T

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Aug 29, 2016

it was a exciting course

By Amit K

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May 24, 2016

Awesome course structure.

By Sean L

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May 21, 2016

very good course about ML

By Mohammad

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Feb 16, 2016

Really Good for beginners

By Amit T

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Jan 30, 2016

Excellent overview of ML!