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

13,228 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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201 - 225 of 3,070 Reviews for Machine Learning Foundations: A Case Study Approach

By Bayardo E Q T

Jul 30, 2020

First of all, sorry for my English. The course is a very interesting first experience with the machine learning and give a base by

all the panorama, the class are funny and easy to understand, and the professors are excellent with the explanation. I really recommend this course for all the people that be start with this new world that is the ML.

Pd. by the way, for the people that no speak a very well English, don't worry, is easily to understand both professors.

By Leonardo M O

Aug 25, 2018

Amazing course. I had already done other ML Courses at coursera, but the competitive differential is the friendly approach took by the professors. Carlos and the other girl are very nice, they the training gets less formal, they look like a friend telling stories in a bar. Another main point is really the uses cases. They swap between the big forest map and the detailed view of the leaf in a succinct way. Easy to understand both views. Congratulations.

By Himadri M

Dec 17, 2015

The Course literally boosts off one's confidence in ML, and gives one the confidence to proceed to higher levels in ML.

Great Course, Superb Instructors and Excellent Course material ! The complete ingredient for a perfect starter course !

I would like to mention that i came to know how to write a review using the words: Great , Good , Superb and Excellent , so that my review is rated above others by the algorithm !

So thanks Carlos sir for this superb course ! :D

By Gur

Jul 10, 2020

A good start to ML. Light on the programming / algorithms but that helps focus on the concepts which is appropriate for a complete beginner to ML.

It took a while to get set up because of the Python and turicreate versions for Mac OSX so I almost dropped out of this course. Glad I kept to it though. Maybe more value in using the standard packages to make skills comparable to others, but I appreciate that turicreate has been optimised for large datasets.

By Abhijit D

Nov 25, 2017

Excellent course presentation by Emily and Carlos - If courses are presented in this interactive manner learning will always be fun and interesting.

Always advisable to have some basics on python , data frame , machine learning(if possible) and you will go really smooth with this intermediate level course.

Course material really good for machine learning with real case studies and capstone project on deep learning was indeed the crown of the course.

By Dave G

Dec 17, 2021

Overall this course was fantastic! The material was taught in an intuitive way with pictures and diagrams that were explained step by step.

My only complaint is that the earlier videos in the course were recorded using graphlab and so there is a little bit of translation to turicreate that needs to be done by the student. It seems that the teachers made a point to update the downloadable notebook files though, so this was not a big issue for me.

By Louis U

Nov 15, 2017

Absolutely awesome! I am really appreciative of the time and efforts on the part of the instructors and the University of Washington to make Machine Learning very accessible. The concepts were very easy to grasp and I endorse the case study approach as a effective introduction to complex topics. Obviously, it will get more detailed and complex in upcoming courses in the specializations but I feel very prepared and excited to learn. Thank you.

By Syed M Z H K

Aug 5, 2017

Thanks alot for this awesome course. As because of it, I was able to learn python (otherwise I used to hate it, when I started learning it with OpenCV) and ipyhon (which is an awesome tool). Furthermore, thanks alot course era for providing me with this amazing fee waiver (since I can't afford this course) , as because of this I am hoping to excel in this field after completing this specialization, in order to later land good job. Thank you!

By Prachur B

Dec 27, 2016

A very practical approach for learning and get excited about Machine Learning. The python notebook exercises really help if you do them diligently (though sometimes it was too easy because of hints, may be hide them and who when someone asks for it). The mention of so many concepts and algorithms can be overwhelming, so a clear guideline on how to leverage the material specifically in this foundation course in the closing remarks would help.

By Aman M

Dec 18, 2018

I was totally new to the machine learning, but this course helped me to understand what is it? What is the importance of it ? where it can be used and what will be the future of it ? There was also enough exercise work to check our understanding to the topic learnt. I think it will be more interesting if they provide a console for code snippet for the assignment... It was very nice experience with Carlos Guestrin Sir and Emily Fox Ma'am

By Tobi L

Dec 6, 2015

I appreciate that the first course focused on applications, I've got plenty of math and programming experience, but I took this specialization to really grok machine learning and its applications. By using graphlab as a black box and focusing on specific applications, I really understood why these techniques are useful. Once I've got the why, I feel much more motivated to dig deeper into the how, which I feel confident enough that I can do.

By Aleksander S

Feb 1, 2019

This is a great course. The content is delivered at a very good pace even for people with little prior knowledge of statistics or computer science — not too fast (would be too difficult) and not too slow (could become boring). Additionally, the assignment model is perfect — it requires completing hands-on exercises, but then the solution is assessed using simple quizzes. Thanks to that the answers and the grades are immediately available.

By George C

Dec 26, 2015

The case study approach and the reliance on GraphLab library makes it easier to get your head around the concepts before going into the detail later in the specialization. I learn better when I have a working understanding of the high-level concepts and the use for a new area of study. This course provides that high-level understanding and the later specializations provides the deep dive. Also, the course seemed well paced and structured.

By Chengcheng L

Dec 27, 2015

This is a wonderful course to get you into the door of machine learning. It covers several key concepts in ML. The videos are easy to follow. The assignments are not difficult to complete if you do the "follow along" exercises. You won't be able to understand the theoretical background of the algorithm very well after taking this course, but you can apply Grahphlab functions to whatever data you have and generate quick and dirty results.

By Evan S

Mar 11, 2019

This course was a great balance between lecture (and lecture quiz) & iPython lecture (and iPython lecture quiz). I like that the answers are multiple choice as opposed to copying and pasting code. That way, any coding errors can be played around with in the notebook first without using up any submission attempts. Emily and Carlos did a great job of keeping the course fun while sticking to the easy-to-understand case-study approach.

By Divyansh S

Dec 25, 2018

I found this course advantageous for me. I found the case study approach of teaching the various concepts of Machine Learning quite helpful. Case Study approach gives us the idea of practical implementaton of these concepts in real life. The quality of the teaching content was very good. Moreover the assignments helped a lot in understanding some of the key concepts. Ideal course for newbies to start learning Machine Learning.

By Matthew S

Jan 7, 2020

A well rounded and not intimidating approach to machine learning. The concepts are introduced clearly and succinctly. The exercises are relevant and digestible. I feel like I have a much better understanding of the concepts to build upon. The only thing I would have liked to see is more outside reading on things that were introduced, but that's also in the next courses of the specialization or just a google away.

By Dhananjay M

Feb 7, 2016

It is an amazing course being taught by professor Emily and professor Carlos. What sets this course apart from any other MOOCs or classes is the case study approach to explain the algorithms. Learning is most productive when a person can visualize what he is taught. This is exactly what this course does by helping students see what they can do with the algorithms they learning with this case-study based approach.

By Allen C L

Jun 17, 2016

A very nice introductory course that uses real-world use case examples to illustrate foundational concepts in machine learning. If, like me, you have only an inkling about what is machine learning, this is a good course to give you a broad overview. Along the way, you'll pick up some very useful Python skills for use in data analysis. You'll also learn to use the nice Python tool, the iPython (Jupyter) Notebook.

By Christopher M

Dec 6, 2018

This was a great course. The instructors were fun and knowledgeable and the assignments were well-written. I loved the flexibility of being allowed to use whatever software I wanted to solve the ML assignments since the quizzes were based on the results of the modeling rather than submitted code. For some assignments I used sklearn and for others I used the software recommended by the instructors (graphlab).

By Joseph C

Dec 5, 2015

Excellent overview course, introducing the ideas of regression, classification, clustering, recommender systems, and a sort of 'short cut' of using the early layers pretrained deep neural network for image recognition as feature inputs into a classifier. Don't expect to get into the 'details' of implementation in this overview course; I believe that level of detail will be covered in the subsequent courses.

By Mitkumar P

Aug 27, 2017

This is a very well designed foundation course in the field of Machine-Learning. This course covers all the important topics of machine learning and data science from classification to deep learning and also consists of fun and interactive assignments. The instructors are very good and they have designed this course very well, I recommend people interested in machine learning field to take up this course.

By Siddharth M

Dec 18, 2015

An excellent introduction to different machine learning algorithms. As expected from an introductory course, this deals with only a top level overview of the tools, without getting bogged down with the details and mathematics of the underlying algorithms. I would recommend this course for those who want to familiarise themselves with using out of the box algorithms provided by different software packages.

By Ferenc F P

Jan 10, 2018

I was hesitating during the review between the 4 and 5 star. The only reason was that in some cases one could obtain different results with scikit learn than with Graphlab. But in the end I gave 5 stars because the course material was good and the exercises were made with real (pre-processed) data. This course is very good for both beginners and those who already have some knowledge in machine learning.

By Parag K

May 23, 2021

Excellent structure of course - presented core concepts in very easy chunks for professionals to ingest, as well as gave real-world scenarios how knowledge can be used. The course dived deep enough to get hands dirty without delving too much into theory which might become useful as student gets overall understanding and then dive further. In all, fun approach with lot of potential for students to grow.