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

13,231 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


Aug 18, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.


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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226 - 250 of 3,071 Reviews for Machine Learning Foundations: A Case Study Approach

By Sauvage F

Dec 18, 2015

Very enjoyable course! Emily and Carlos actually succeed in giving a more than useful overview about so many kinds of tasks, algorithms and concepts of machine learning in very short time (given the material to cover!). I really loved the topics of the hands on assignments.

New to Python (I'm a R user most of the time) I also learned a lot about "the other language" of Data Science. Thanks a lot!

By Christine S

Nov 9, 2015

Course is well organized, lectures explain learning concepts very well. And using python notebook examples to show machine learning uses are very unique and quite easy to follow. The assignments may not have been as challenging as some other school's courses, but overall, this is a great course for those who would like to have a practical approach to apply machine learning to solve data problems.

By Christopher O

Jun 2, 2017

The course is very organized and begins as a good introductory module. The difficulty increases throughout the course, but you are given all of the information and tools you need to respond to questions, unlike some other ML and engineering related MOOCs. You also learn some techniques that are actually useful and entertaining, like scouring wikipedia to find how similar people or articles are.


Apr 11, 2016

This is course is great and the way its been taught by professors is very cool :)

I am getting to know the use case and than how we are going to do it, rather than conventional other way round. I am so happy that we first come to know about the application and its so important for a student to know that.

Thank you so much, only problem I see is that this course should have been started earlier :)

By jackytu256

Dec 15, 2015

Its a course that provides a basic concept of what the ML is such as linear regression, classification, clustering and etc. This course not only offers the general idea for students but also implements Python-based code ( based on the all of case studies), which is an efficient approach to let me real know what the lectures talk about. Its a real nice course for the ML entry-level students.

By Konstantin G

Dec 31, 2015

Great course! Thank you guys for have been made such an easy to understand way to understand basics of ML.

The thing to improve: some assignments didn't explained in the course and I still don't know the way to discover the correct answer for the assignment for the deep learning module. The question about where the simple classification can be applicabable and there is a list of functions.

By Ellen R

Jun 3, 2018

I loved this course. It did a great job of getting into really interesting applications of machine learners but staying accessible for people without a lot of previous programming experience or technological knowledge. I'd really recommend it for anyone who wants to get a well-grounded sense of some of the principal machine learning techniques that are changing the way the world works.

By Enrique C M

Dec 5, 2016

Brief but very good overview to typical Machine Learning models that are currently being used in many real applications. Nice and easy going teaching model based on case studies and lots of examples and practice during the assignments. For being only an introduction to this world, it was a quite interesting intro and now I am keen to follow up with the next parts of the specialization.

By Bhisham J M

Sep 22, 2018

I found course content and they way it is designed is perfect for anyone to easily grasp the concepts. I am from non-development background and don't have much grip on python language but it was still smooth and easy for me to progress this course by learning python basics and commands as well which is required for programming assignments. Well done coursera, keep up the good job!

By William O

Feb 27, 2017

I'm just a high school student who wants to attend University at either UC Berkeley or University of Washington as a computer science and physics double major with focus in quantum computing and A.I.. This class was a great introduction into the world of A.I. / Machine Learning. I would highly suggest taking this class to get a start with simple programming and machine learning.

By Nikhil S

Apr 20, 2020

I would give it a five star if I had not faced problem while installing turicreate but then I continue course by using graphlab but it is not free and I do not have license so I cant use it for any other purpose like to build personal things. Also I would like to build my own model rather than just implimenting a built one.

But I think it was a nice course to start in the field.

By Sherry A

Sep 13, 2017

This course is excellent. I am astounded at how well programming techniques and concepts can be taught in a MOOC. I wish these tools were available 20 years ago when I was first learning programming. The instructors were wonderful, too. I was particularly impressed with the clarity of the explanations. The assignments were challenging, too. This is not a course for slackers!

By Abdallah G

Jan 18, 2017

This course was really good I found it a really good start I also really like the way in which Emily was giving the theoretical parts then Carlos follow her with the practical part . Also Emil and Carlos have a really Excellent way in explaining the course material which make it really entertaining .

Thank you so much I really loved and Appreciated every part of the course.

By Pratyush D

Oct 6, 2015

I have been halfway through the course and it is excellent if you want to build strong foundations in Machine Learning. A basic level of experience in programming is required. Python is the medium in which programming assignments have to be done. But if you have a certain level of proficiency in any language then you won't have any problem. Anyways, great going till now.

By vinit s a

Nov 23, 2016

Amazing course. I would recommend this course to get started on machine learning and understand its applications. This course doesnt go deep into the complex mathematical theory unlike others and the instructors do a very good job in going through the material systematically. Assignments are a bit challenging but manageable since graphlab has a lot of inbuilt libraries.

By Udhay

Dec 8, 2015

Excellent course to start the ML concepts ! The Case Study approach really gives a deep insight into each concept discussed. Looking forward to further courses in this specialization !! My python programming knowledge really helped to complete the course few weeks earlier !! Suggestions : An example using the python ML modules -sclearn,numpy,matplotlib will also help !

By Julius L W

May 13, 2021

I really recommend this course, it consist of easy-to-understand theory and the hand-on also very easy to follow. Having basic to intermediate level of python really helped you to progress this far, and if you always use Pandas, Turicreate and SFrame data manipulation could be a new learning curve for you, but again everything is google-able. Thanks for this course!

By Sruti R

Feb 21, 2018

If you are looking for a course to find out what machine learning is. This is a great course. I only completed the first course so far and It has given me a basic understanding of what machine learning is about, the basic techniques, introduction to software used for machine learning and a look at what's ahead to deepen the learning if I choose to pursue this line.

By Theodore G

Oct 23, 2016

A really interesting, introductory course in Machine Learning Methods and their applications. The case study approach followed by the instructors makes it ideal to learn how these ideas used in real-life problems. The programming language used is Python (GraphLab Create or Open Sourced libraries), which is most probably the best choice for newcomers in the field.

By Fabio P

Mar 25, 2016

After going on with the specialization I started to understand how great this first course really was: It teaches you lots of basics while not expecting too much and shows how you can use machine learning in different scenarios.

On it's own it's possibly only worth 3 stars, but in the context of the whole specialization and further courses it's definitely 5 stars!

By Paul P

Mar 7, 2017

This course is great for anyone who wants to not only get a great overview of the concepts of machine learning but apply the concepts and see the results in week 1! You'll be using machine learning algorithms to train models with real data even if you have no idea what that means! If you're taking the time to read this review you should probably take the course!

By Matthew B

Jun 4, 2016

This is a great class! Highly recommended. Emily and Carlos are a great team. The videos are polished, the progression through the material is well organized and everything just fits together very well in this specialization. The assignments are challenging enough to be worth the effort. Great specialization... I look forward to completing every class.

By Stefan K

Dec 28, 2015

Very good introduction to machine learning, the teachers and assignments are very well planned and executed. It is a course where you can spend more time, as the workload is bigger than at usual courses, but you learn a lot every week. I am really fan of this Specialization and I plan to complete the whole Specialization given enough time in near future.


Oct 7, 2017

You were very near to be the legend of Machine Learning but after cancelling the capstone project you aren't. I'm really disappointed, why great things always not completing. I wish you to provide us with at least IPYNB for the capstone project because that will help us a lot. Finally, this is a really amazing course. Thank you for this great course.

By 이제민

Dec 13, 2015

that's awesome course. They bridged between theorem and practice. so we can imagine and know how to apply machine learning algorithm in real world and real problem. what's more, we can adjust and tuning machine learning algorithm to make customized algorithm. I am very confidence this course should give everyone great opportunities and good insight.