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

4.6
stars
12,429 ratings
2,974 reviews

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

PM
Aug 18, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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

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2401 - 2425 of 2,887 Reviews for Machine Learning Foundations: A Case Study Approach

By Philip L

Nov 12, 2016

Some quiz questions' answers are incorrect and instructors need to update the quit to reflect correct answers

By Islam M

Apr 19, 2016

-great introduction .

-go through a lot of exciting topics.

-but the implementation part is boring something.

By M.sakif m

Nov 16, 2015

Interesting class, but should have used open source python libraries instead of restricted license libraries.

By MOHD S M

Sep 15, 2019

Amazing they guide me help .Special sir working on project .It clear my concept with the real world example.

By Vasudev V

Feb 19, 2017

It would be great if you could intersperse theory and practical sessions. Otherwise, a very useful course...

By Mohit P

Mar 12, 2019

This course is a great starting point who has no earlier experience of ML. . Cheers to the course makers!!!

By Yuting S

Feb 26, 2019

Wonderful course.

The only problem is that I can't review the course materials after completing the course.

By C K S

May 28, 2018

Course was nice and especially special thanks to both the faculty's who make us to understand the course.

By Benson C

Sep 4, 2017

Interesting, I never used graphlab before. It would be better if this course went through algorithm deeper.

By Javier M

Jul 17, 2017

Great introduction to the topic. I think the juniper notebook is still buggy. Its stability can be improved

By Sergio A M

May 14, 2016

It is a great introduction but this could be done by adding a week more in each of the following courses.

By Veera R

Mar 11, 2016

Case study approach followed in this courser is very use full and helps to understand the methods better.

By Clotilde D

Jan 5, 2016

good overview, a little bit hard regarding the deep learning course, that would require more explanations

By Hitesh D

Oct 1, 2019

The course has helped me understand basic of Machine learning and has created interest for me.

Thanks :)

By Pushpak T

Mar 13, 2016

Gives a high-level understanding of machine learning concepts and focuses a lot on its application part.

By Rajkumar D

Mar 2, 2016

Good Start up with case study approach to just understand what we gonna learn in further specialization.

By ashish

May 2, 2018

The course from coursera was well delivered. Albeit, their seems to be too much dependency on graphlab.

By Mohit A

Nov 12, 2019

Great learning experience! We should have option of videos with exercises using Pandas & scikit learn.

By Michele P

Sep 5, 2017

Good introduction, although the implementation exercises use mainly GraphLab which is not open-source

By Santosh K W

Feb 16, 2016

Useful course. Covered good amount of usecases for Machine learning concepts with handson experience.

By Cristian C H M

Jun 20, 2020

The videos are out dated and Turicreate doesn't work on Python 3.8 and the latest version of Ubuntu.

By Jeff L

Nov 29, 2015

This was a fantastic course for someone both unfamiliar with machine learning algorithms and python.

By Tomáš Z

Nov 30, 2018

Nice intro but it could have been more in depth, not just as a simple graphlab/turicreate tutorials

By Jonathan E

Aug 15, 2017

I liked the interactive python programming however the course could be more rigorous for my tastes.

By Sergey G

Mar 23, 2019

It'd be great to get rid of showing how teacher type code or write something, it's kind of boring.