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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


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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.


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Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

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1 - 25 of 3,119 Reviews for Machine Learning Foundations: A Case Study Approach

By Ravi P

Sep 8, 2018

The materials used in this course are extremely outdated. In order to access the data to do the projects you have to use SFrame, which is only supported up to Python 3.4.x. Python is currently on v 3.7.0. The data should be provided as .txt or .csv to be more universal. The instructors claim that you don't have to use a specific library to do this course, but you have to have at least SFrame in order to access the data! Further I am sure SFrame and Graphlab are good tools, but the course should be taught with open source tools so that the students can continue to use those tools after the course is over.

I wanted to like this course. I did enjoy the professor's teaching styles, but the fact that I would have to download a new outdated python environment, and non universally accepted tools, to even access the data is a major deal breaker!

By Ibrahim M A

Apr 29, 2017

My only happiest moment in this whole course is writing this review, I couldn't wait to finish it in order to give it the 1 star rating it deserved.

What I've seen from this course so far is abandonment , that's right this course is abandon ware, no questions get answered on the forums (asked a question a month ago and still didn't get an answer) and the links are outdated (links to further documentation don't work).

I wouldn't recommend this course to anyone wanting to learn Machine learning since the instructors use proprietary libraries that need a license to use outside this course thus application wise what you learn her isn't transferable only the conceptual content;however, even in that there isn't much content for, since everything is an introduction here so nothing is quite useful .

If your on a tight budget and your taking this specialization you could skip this course. Actually you could even skip this specialization since they canceled the capstone project so investing any money and time here is a waste. I can only recommend this specialization/course IF the instructors add a project at the end , be more involved on the forums , update non functional links ,and finally USE NON PROPRIETARY libraries hence they will need to take feedback from the students and redo most components of this specialization.

By Oscar R

Oct 24, 2018

I spend two days trying to get the graphlap lib working on two OS, and could not. I had to spend couple of hours setting up the aws services to be able to work with the samples.

Phd's I dont think they make good teachers....


By Jatin P

Mar 28, 2018

To follow along the course you need to install Graphlab library, which is the biggest challenge. Also, the support you get from the creators are not good enough.

I regret to waste my time on this course.

By Sourav S

Jun 27, 2018

Too dependent on Sframes and graphlab which does not work most of the times. I had to spend an entire day just figuring out versions of python to make this work.

By sreeraj

Jan 21, 2018

Such a bad presentation with no help to people with graphiclab tool setup.

By Andrew

Nov 4, 2017

Requires software called Graphlab Create that would not install on my machine. Unable to complete any of the course material due to this.

By Elvin V

Oct 26, 2017

The worse course I have ever taken on Coursera. Forcing you to use their own library which is also not open source and free is ridiculous! You will never use graphlab in the future and there are better alternatives available! Totally useless experience. And most of the time vide lectures are just some mumbo jumbo, like showing diapers or napkins for 2 minutes! I have successfully wasted a lot of time on this course.

By Hernan M

Sep 25, 2017

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.

Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.

Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.

Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.

By Gianmaria M

Jan 22, 2019

Very relevant material clearly explained by the professors, who are very knowledgeable and engageing. However the installation and usage of the GraphLab module is cumbersome and plagued with bugs. This could still work if there was enough support however I did not find any helpfrom the mentors/tutors who simply did not answer my questions in the Forum thus making my experience even more frustrating. Pity, I certainly hope Coursera can fix it as the class is quite good

By Muhammad W K

Jul 21, 2019

A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!

By Sharina C

Feb 9, 2019

I am extremely frustrated with this course. I have spent sooo many days just trying to get the software set up. It's currently week 3 and I can't complete week 1. I've followed the directions and run in numerous roadblocks, some of which I was able to resolve after searching through the course forums. I shouldn't have to scour the forums to get setup...the instructions should be updated. Unfortunately, I'm still stuck in week 1, unable to get the software running properly. It's really frustrating.

By strx

Apr 12, 2017

Maybe a good course, but you need to be an IT crack to be able to install the software and make it works. Online help does not help. Irritating! 45USD lost. I don't recommend this course.

By Rahul D

Dec 23, 2017

Course uses proprietary packages. Better learning from "The Analytics Edge" conducted by MIT at

By Walther A G L M

Jul 2, 2018

relying on proprietary library and unreliable notebook made this experience painful

By Matthew H

Oct 26, 2017

I just completed the first week of this course and am choosing not to continue. The first week consisted of 75 minutes of video in which we learned a half dozen facts regarding Python syntax and the use of SFrames. This content could have been presented in a single 5 minute video with just a little planning and editing. I realize that the presenters perhaps wanted to ease folks in, but this is silly. There may be good content in the following weeks, but I am not patient enough to find out. Gonna try a different ML class. Sorry guys.

By lianghui t

Mar 9, 2019

the graphlab can not be installed

By Mike C

Jun 14, 2016

The course is basically an advertisement for the software one of the teachers created. I did learn a bit of high level concepts, but when it comes to coding, the answers is always 'conveniently, my software does this for you'. I wanted to actually learn about these concepts deeper, and implement them.

I also was able to complete this 6 week paid course in a few days which should not be possible. I have since started the free Stanford course taught by a co-founder of coursera, and it is MUCH better! I recommend it to anyone!

By Eugene K

Feb 10, 2017

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.

Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.

By Theron R F P

Nov 4, 2017

Good intro to the ML concepts, but my review is negative due to :

By Shane C

Nov 16, 2016

I did not find the course very good. I came into the class with only real basic knowledge of Python and I was hoping to be able to pick up more as part of this class. WHile I might have picked up more, it was only because I used resources outside the course.

The video instructions in programming in Python left quite a few gaps to figure out by reading documentation. The videos themselves were divided into two sections -- first a theoretical or classroom like section and a second a lab/programming section going over some coding in Python. The 'classroom' type lectures were pretty reasonably good. But the lab/programming were pretty terrible.

They instructor really break down the syntax of the code and just left the student to figure it out. This made being able to take this code and to adapt it to other uses very difficult.

I would not recommend this as a course to help learn Python.

By Jonathan W

May 31, 2019

The course includes some good, basic, information on machine learning. The instructors seem to know the material well. However, the exercises and coding are based on a python package written by one of the authors that, while free to students, does not easily translate into common packages such as Numpy, Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and Pandas. Also, the package used only works in Python 2 (which will no longer be supported as of January 2020).

By Pritish K

Apr 7, 2019

The most useless course on Coursera. I have wasted 3 weeks just trying to install Graphlab and the installation seems infinitely tedious. There is no support from Coursera or University of Michigan to install the software

Why do they insist on teaching on a software which have so many known issues and so many students are struggling to install the software.

The objective is to learn data analytics and machine learning, not to become a systetm admin and n IT guy.

By Valentin T

Jan 17, 2016

Using a proprietary library instead of widely used libraries and discouraging the use of open source widely used libraries. It barely compiles, the example notebook has method calls that use non existing methods of the SFrame object.

The course claims that it teaches the student how useful practical knowledge but then ends up using a non standard library and saying not to use pandas or scikit learn.

By Ivo R

Nov 22, 2019

This course is very frustrating because it uses a library called Turi Create that can't be installed on Windows 10. There is no support on how to setup you local environment after three days of frustration I decided to cancel my subscription.

When I opened the forum for week one all the threads were asking the same question: "How to install Turi Create on Windows 10."

It would have been much better if the course was done with a more popular library like Skit-learn.

This course is useless if you don't use a Linux or a Mac