Chevron Left
Back to Machine Learning Foundations: A Case Study Approach

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

12,428 ratings
2,973 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

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

Dec 19, 2016

Great course!\n\nEmily 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.

Filter by:

2426 - 2450 of 2,887 Reviews for Machine Learning Foundations: A Case Study Approach

By Ruben E H Q

Dec 11, 2016

Great introductory course for beginners to machine learning and I loved the programming tutorials.

By Alfonso M

Mar 26, 2016

Instructors are clear and passional about their teaching. I wish the course was slightly deeper.

By E. M S

May 17, 2017

Good overview and programming warm-up. Just need to change the links to from


Mar 17, 2021

Nice Course and developed a nice understanding from the questions present in the assignments.

By Tomas O

Nov 8, 2019

It has a lot of problems with the programs you should install. But the content it's amazing.


Nov 14, 2017

This course was a very good introduction to some of the techniques used in Machine Learning.

By Stefan S

Jan 4, 2016

Good intro to topics in machine learning as well as to Graphlab Create and iPython Notebook.

By Stanislav B

Apr 18, 2020

There were unnecessary problems with data. Last course (Clustering and retrieval is better)

By weimin l

Oct 27, 2016

A very interesting course! I learned a lot. Will continue on the next course once it ready.

By Rishabh P

Jul 26, 2020

It would be great if you start deployement of things in python also not only in turicreate

By Brent R

Jan 29, 2017

Good intro to ML, but would've enjoyed less of the "Black Box" approach in using Graphlab.

By Aaron W

Jan 1, 2016

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

By Patrik L

Jul 17, 2017

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

By Tung N

Jun 7, 2016

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


Jun 3, 2016

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

By Thomas S

May 29, 2016

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

By Suneet T

Feb 7, 2016

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

By Franck B

Jun 19, 2020

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

By Shankarganesh G

Sep 11, 2019

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

By Piyush M

Mar 21, 2017

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

By Krishna P S

Dec 13, 2018

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

By Jay D S

Sep 18, 2020

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

By Ibrahim G

Aug 29, 2017

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

By Deleted A

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

Jul 15, 2017

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