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

12,453 ratings
2,978 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

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.

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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2676 - 2700 of 2,891 Reviews for Machine Learning Foundations: A Case Study Approach

By Carin N

May 22, 2019

Its a fine course but most of the coding comes from the program Graph Lab, which is only free for academic purposes. So you won't be able to take your skills outside this course unless you 1) do all the HW assignments in an open-source and struggle (because there is no assistance for this method) or 2) you pay for GraphLab once you are done with the course (not worth is with all the open source packages out there). The instructors also don't make it easy for users to use the open source packages because Graph Lab splits the data differently than these other sources, making our answers always slightly off.


Nov 11, 2015

The video lectures provide a clear and concise introduction to interesting topics in machine learning (ML). However, the exercises are very general and use 'black box' ML algorithms for most of the solutions. For me, the exercise structure was more confusing than educating. I am aware that this is the intro course to the specialization, and I am looking forward to actually building the algorithms in the future courses. Too bad you can only take the entire specialization over the course of ~6 months, and not at your own pace! Especially since the homework is checked automatically.

By Albert V

Dec 8, 2020

This is superb introduction to Machine Learning. I've tried to read the "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" but can't understand the ideas in the book until I've finished this course. Overall, this is a great start for those who want to learn Machine Learning concept.

The downside is it uses non-standard Turicreate rather than popular Sci-kit Learn, or Tensorflow. But as they said it is more easy for a beginner to grasp the concept using Turicreate than sci-kit learn which is true.

By Philippe N

Apr 5, 2020

The course gave a great overview of Machine Learning through case study and will help me a lot I think to design similar courses in the future. On the bad side, I have noticed the course was developed some five years ago and that the videos were not updated. The fact that for instance Graphlab changed to TuriCreate is annoying since we have videos and the notebook does not correspond to it. Furthermore, The mentors are not responsive enough on the forum. I have an unanswered question and noticed many other questions were left with no answers.

By Varun R

Jan 4, 2016

I really liked the fact that we were given an overview of all the machine learning techniques before we actually delve deeper. However I would have rather appreciated it further if we used open source python libraries rather than graphlab!

I think the use of graphlab really did limit our scalability and use elsewhere other than on the course.

Please do consider using open source tools in further courses and also provide starter code for the assignments in one open source library in addition to the code provided using graphlab.

By vitali m

Mar 7, 2018

Although the concepts presented in the course are interesting, all course examples are based on a proprietory python library (Graphlab) which you are most likely will never use in real life. As the course suggests you could use open source libraries (scikit for ex.) but since all examples do not use it, it will take 2-3 times more time to figure out how to do the same assignment using open source libraries. So if you hope to learn ML concepts applied to scikit, pandas, etc. that's probably not the best course for it.

By Kelsey H

Dec 31, 2019

Very frustrating. This course is a good Machine Learning overview, and light on programming. BUT the homework is based around an opensource library, TuriCreate - this is only available for Mac OS. Windows users will have a harder time with this course.

The workaround I found was to register for a student version of GraphLab (which the course previously used). I used an older version of Anaconda that I got from the GraphLab website, and modified the homework assignments to use GraphLab instead of TuriCreate

By Pier L L

Aug 10, 2016

Nice overview of the specialization. Since it aims at showing the advanced and interesting things you will learn during the specialization, some of the practical sessions are way too advanced. Thus, for me felt more like a mechanical copying of what the instructors did rather than an actual assessment of what I understood. Also, since some of the applications are actually repeated at the beginning of the main courses, it feels like a repetition somehow when then you move to the specialized courses.

By Kevin C

Oct 5, 2020

I really enjoyed the case study approach that's why the 3 stars but I'm not gibing it a 5 because some of the videos could just be skipped because half of them are the instructors laughing and the other half is some important info. Also it looks like they don't really care about the community because not all questions asked in the forums get answers. Finally, there are some clear mistakes in the Quizzes that haven't been resolved although many people have complained in the forums.

By Andrey B

Jun 4, 2016

The course could have been marked by 5 stars if it weren't for the promotion of a commercial Python library developed by one of the speakers. There is no way a student could complete the course without having Python installed and a free licence acquired from

Students should be able to use any programming languages and scientific libraries to do their homework and the subsequent courses of the "Machine Learning" specialisation are excellent examples of such approach.

By Jakub V

Sep 1, 2018

I was unable to get graphlab running – had to use turicreate instead. Also, the most interesting part, deep features, came a bit "ex machina" – without a proper explanation how to create what was prepared. Also, I really miss the parts 5-6 of the specialization which look very interesting. The basics are already well covered at many places. If the parts 5-6 were existent, I would probably take the whole specialization. This way, I will pass.

By Christopher O

Nov 7, 2016

I enjoyed the course and I will continue with the specialization. I am giving a 3-star rating as i) the lectures need to be updated with correct data or need to provide guidance as to when one should expect individual difference when following along with the notebook, ii) instructor / mentor response in the discussion forums is lacking, iii) graphlab is now an outdated tool as it is not commercially available.

By Konrad Z

Aug 14, 2017

It would be better for the course to focus on using scikit-learn for machine learning. The course focuses on using GraphLab (, which is a commericial product, free for academic use. I'm doing this course for professional purposes and my preference is to gain familiarity with free/open source solutions that I will be later able to utilise in production environment.

By XingliangZhao

Dec 24, 2019

To be honest, this course is not friendly to windows 10 users because it forces students to use the apple Inc's Turicreate instead of the most popular sklearn. Admittedly, windows 10 users can still install the Turicreate by WSL but not everyone wants to add a subsystem to their windows just for this course. Except for this, this course has a nice structure and the content is really practice-oriented.

By Chris T

Jan 18, 2017

I found the Course very interesting, well prepared from the Tutors and I liked the case study Approach since it provides actual examples where Machine Learning can be realized. I am interested to enroll in the second Course of the certificate to validate if it will go into more Details and Background regarding the build of the algorithms theoretically and in Python. I would like to thank both Tutor

By Manuel O

Aug 31, 2016

While I am aware that this is an overview of the other courses in the specialization, I felt that the quizzes and programming exercises didn't really get into the actual topic. For example the recommender systems quiz and programming assignment have nothing about factorization except a single superficial question. The material is clear and the overview is nice, but the practical part let me down.

By Jess T

Aug 29, 2017

A nice ML overview that introduces many tools without going into detail on how they work. Pro: Loved the programming assignments, nice Jupyter notebooks. Con: found the constant hyping of the Capstone course (which got cancelled) frustrating. The GraphLabCreate software was neat to see and easy to use, but ultimately I preferred the more first principles approach of Andrew Ng.'s ML intro course.

By Dheeraj A

Nov 27, 2016

This is a good introductory course, however the quiz questions and over dependence of graphlab are off putting. The instructors share good insights about the need and motivation for various ML techniques. I wish there was more support on the project using pandas and sklearn. Graphlab is immensely powerful, however not adopted in industry making it hard to apply the learning in real world.

By Christopher W

Mar 6, 2016

Pretty high level overview. I guess it's necessary to give a roadmap for where the concentration leads, but I wonder if each lesson couldn't have been added in its respective module, or if at least the Foundations Module couldn't be shortened a little - or alternatively made a bit more challenging. I'm on the first real module now and the change in difficulty is quite significant.

By Sander v d O

Apr 1, 2016

This course is for you if you really don't know anything about Machine Learning and nothing about Python. If you do know something about it, look for a different course.

I learned the most from lesson 5 and 6 about recommenders and deep learning because I knew nothing about these subjects.

The programming exercises are disappointing: just cut and paste. I found this demotivating.

By Sean I

Nov 5, 2017

I wish they used open source tools for this. I will not be paying for a GraphLab account nor do I see myself using it in the future. I felt less inclined to strain over learning the API and was unused by the technologies. Other than that the course is pretty interesting as I was able to apply some cool data analysis using ML practices I've learned in other Coursera courses.

By James H

Jul 1, 2020

The course was good, and the instructors did a good job. There don't seem to be any mentors in the forums who are helping, and the library used for the exercises was changed from the one in the lectures. The specialization seems to have been abandoned before they published courses 5 and 6, so ignore every time they talk about how great the capstone project is going to be!!

By 周玮晨

Jun 28, 2018

Lectures are great. Unfortunately, i can' t install graphlab create on my windows 10 labtap.I wasted two whole day on it!!!!! I tried every methods google told me, all fail or with bugs. I think pandas and sklearn are far more user friendly.不建议大陆使用windows的朋友尝试安装graphlab create,标准安装方式即使用了VPS也网络链接失败,用anaconda安装的话,anaconda3可以安装,但是没有canvas功能,anaconda2各种奇怪报错。搞了两天失败,我还是用sklearn。

By S M R A

May 9, 2020

This course needs to be updated. Windows don't support TuriCreate or Graphlab. Because it works on python 2. But now python 3.8 has come and TuriCreate doesn't work in it either. So, I had to use Ubuntu in my virtual box to work on the assignments. The course wasn't bad. But if they update the course, it will be a great one for beginners in machine learning.

By Katya H

Apr 26, 2016

I think it was a good introductory course. However, I think it was too simple: assignments required no more than copy+paste from the lectures.

I understand the primary goal is to hook people up on how good graphlabs is, but I'd rather leaarn numpy, sklearn and other widely available tools. At least show both in the leactures. Please :)