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

4.6
stars
12,361 ratings
2,960 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

SZ
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.

BL
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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2626 - 2650 of 2,874 Reviews for Machine Learning Foundations: A Case Study Approach

By Shubham D

Dec 3, 2016

nice course

By Le H P

Aug 16, 2019

well done!

By Daniel Ø

Jan 18, 2016

very basic

By Muhammad A K

Nov 27, 2020

very good

By Sayam N

Sep 25, 2020

Excellent

By Aishwarya S

Jul 5, 2020

very nice

By Zhen W

Jul 5, 2017

Good ~~~~

By Kevin C N

Dec 10, 2016

Thanks!!!

By Oriol P

Mar 30, 2016

Was nice!

By Sreemannarayana B

Feb 23, 2016

Excellent

By Oumar D

Feb 21, 2016

Efficient

By DEBASISH M

Sep 21, 2020

Like it.

By John M

Jul 4, 2018

Liked it

By Phoenine

Dec 23, 2018

So good

By Deleted A

Aug 14, 2020

good

By YEDURADA J K

Aug 10, 2020

nice

By Rohan B R

Jun 24, 2020

nice

By Lucky V

Jun 21, 2020

cool

By Dr. A S M M R

Jun 6, 2020

Good

By 楊傑綸

Dec 29, 2015

Cool

By 王博

Nov 13, 2015

nice

By Brijmohan S

Mar 22, 2018

V

By Sofia P

Mar 12, 2016

I did not have a lot of experience in machine learning, so this course was very good in the aspect of introducing people to machine learning concepts. Most of the times the material was very well explained, and I like the concept of the tutor writing on the screen at the same time they are presenting, personally it helps me more. Some of the quizzes were easy so you did not need a lot of preparation, some of them were more difficult or troublesome, like the quiz for Deep Learning. I also liked the graphlab module, I think that learning how to use it will help me with my own work.

However, as this course does not really go in depth in the algorithms themselves, I feel that after one month and a half I have a basic idea, but I haven't learned much about how to implement machine learning on my own even in basic things, while other courses have more or less the same time frame and are more dense in their material. In my opinion, this whole introductory course would just be just splitted and each of these intrductory weeks would be appended as the first week of the subsequent modules to come. Because anyways, after 4 months in the specialization, if somebody continues to the recommender systems module for example, he/she would have forgotten the basics of this so they would need to cover again the recommender systems week in this course. And from the other hand, if some introduction is again repeated in the subsequent modules, then why have this introductory course anyways?

Thanks.

By Denys G

Jan 13, 2016

The biggest downside of the course is that instead of learning on open source machine learning modules (sklearn) the course offers Dato's GraphLab, a proprietary piece of software that requires paid licenses to operate.

To be clear, during the duration of the course students can use a student license that provides graphlab for free but this expires after a year. It seems like fine software but if you arent going to purchase a license after the class expires whats the point? Also, Graphlab is built on top of python2.7. If you are running python 3.0+ on your machine youll have to install a python 2.7 instance.

Otherwise the quality is solid. The philosophical approach the professors take is to give you a taste of a variety of machine learning models. The upside is that if you want to get a taste you can. The downside the course feels pretty shallow and then the next course in the specialization -- regression -- feels like a pretty stark contrast. In general it could be argued that this is a problem with all coursera courses. How do you modulate course difficulty when you could be targeting students who are somewhere between high school kids to computer scientists? So the course and the specialization tilts between very easy and very hard.

By Steven D

Sep 11, 2016

The course is effectively a tutorial on how to use proprietary software to solve a range of machine learning problems.

I liked the fact that the course covered a wide range of problems quickly. There were however two issues that I did not like.

1) It is not well supported and given that the technology is proprietary, there are few other places that offer support (i.e. you can’t just look at problems and solutions on stackoverflow to get insight into the tech)

2) For a course labelled as “intermediate”, it presented very little detail. Most of the course was dedicated to explaining particular problems, the solution to which was inevitably “then you train this really clever, one-line algorithm we have written for you and you query it for insights”. I felt a little cheated by this approach to a subject which should be really fascinating.

While some of my concerns may be addressed in follow on courses, I am left with little insight into what really lies ahead. For example, is this really an “intermediate” course? What background do I really need? Will we ever get to the detail or will I always just be expected to call someone else’s brilliant algorithm and accept the result.