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

4.6
stars
13,531 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

RM

Feb 2, 2022

I was very disappointed with the exclusion of the final courses and the capstone project. The most interesting part of specialization no longer exists and no plausible justification has been given.

RH

Jun 8, 2017

I felt this course did a good job introducing the student to Machine Learning. The examples and hands on assignments brought the concepts home. I was able to use the knowledge immediately at work.

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2651 - 2675 of 3,157 Reviews for Machine Learning Foundations: A Case Study Approach

By Aaron W

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Jan 1, 2016

Great introduction to ML concepts! I'm hoping to dig a little deeper in the next courses!

By Patrik L

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Jul 17, 2017

Would be very useful to have the option to see all the coding examples done with sklearn.

By Tung N

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Jun 7, 2016

Very good balance of concept and practice. Would be 5 stars if all tools are open source.

By Kai S E

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Jun 3, 2016

A Brief understanding achieved! I love the instructor and the way they conduct the class.

By Thomas S

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May 29, 2016

Nicely structured and overall a great course. Sometimes a little slow, for my preference.

By Suneet T

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Feb 7, 2016

Very good course for getting a high level understanding of the Machine learning concepts.

By Franck B

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Jun 19, 2020

Very interesting, didactic but a little bit too based on a specific non-standard library

By Shankarganesh G

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Sep 11, 2019

It' s really worth spending time to learn this course. It's very informative and useful.

By Piyush M

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Mar 21, 2017

Very good introductory course with an excellent mix of theoretical and hands-on content.

By Krishna P S

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Dec 13, 2018

course is really good with real life examples. Able to correlate well with the concepts

By Jay D S

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Sep 18, 2020

this course should include some more coding about python in manchine learning and knn

By Ibrahim G

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Aug 29, 2017

it's very cool base and i hope next specialization course will get more into details.

By Deleted A

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Oct 23, 2017

The first week was a little chatty but the content of the rest of the weeks is good.

By Chin-Teng H

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Jul 15, 2017

bomb bad awful interest present immutable sad great time tack how hungry hungry opps

By Hakim L

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Dec 3, 2018

Good course despite the technical issues with GraphLab Create in Coursera Notebook.

By Chenkai Z

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Oct 9, 2016

Good on presenting and using ML tools, but the part of principle is not good enough

By Mateusz B

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Dec 31, 2015

I enjoyed this course but I think assignments could be a little bit more difficult.

By Damián J S

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Feb 25, 2025

great course overall , just a little bit outdated ! but great course to start with

By Kim K

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Mar 23, 2016

a very good introduction for machine learning with good examples and explainations

By Alireza A

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Sep 13, 2021

good course, but the diffrence between turicreate and graphlab is a bit suffering

By Shyam A

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Jul 7, 2020

good, But check whether your pc can run on graphlab before taking up this course.

By Sachin R G

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Jun 13, 2020

Need some improvement like much more focus on statistical concepts behind program

By Shashikant K

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Jun 9, 2020

This is very good course. This is helpful for me. Some problem on using graphlab.

By Mich

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Jul 1, 2021

The course needs to update the laboratory files, since the commands are outdated