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

4.6
stars
13,374 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

PM

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.

SZ

Dec 19, 2016

Great course!

Emily 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.

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3001 - 3025 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Nasimul J F

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Aug 16, 2020

THANK YOU.

By Kai C

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Nov 24, 2015

Too easy

By Geetha G

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Aug 16, 2021

good

By Anshu R

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Sep 12, 2020

good

By 18103048 H - S C

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Sep 4, 2020

Good

By MD. S K S

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Aug 23, 2020

cool

By tarun v s n

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Jul 23, 2020

Good

By Abhinav S

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May 10, 2020

good

By Bindra B

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Jun 21, 2021

k

By CHEE W M

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Sep 26, 2019

V

By Andrew S

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Dec 3, 2016

The content of this course is interesting, I liked the examples, and the material gave an interesting overview of different aspects of machine learning. From that perspective, the course is as advertised. But, where this course goes wrong is value for money - it is very superficial and not worth what is charged.

As noted by others, this is not a course for learning so much as an advertisement for the instructor's own pay software and their other Coursera courses. I'm not against that per say if it was entirely free, but charging for an advertisement is ridiculous. In my case I thankfully started with the free model so I didn't lose out, but I could see others being dissapointed. I strongly recommend starting the material with a free signup and only pay if you really want the extra grading.

My other main problem was with the pace and detail in the course. I would have liked more detail, but I recognize this was intended to be a high level view so I'll live with that level of detail. The material covered, however, does not need 6 weeks worth of lectures. This course could be ~1/2 as long, cover the same material, and be a MUCH better course.

Other small problems include some poorly edit videos (there are a lot of examples of simple stumbling in the videos that should have meant they do another take), very short videos (maybe a person preference, but the number of <2 minute videos here is annoying, especially when there's a 5-second standard video at the start and end of all videos). All in all, there's just a lot of wasted time.

When signing up for this course I was really excited for the entire specialization - now, not so much. I'll probably try the second course in the series (for free to start) to see if things improve, but ironically this advertisement video has if anything turned me off their other products.

By Jean T

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Apr 17, 2017

Con:

(1) I feel I spent most of the time learning graphlab. Suggest replace it with standard Python as the standard tool for this class. Provide any needed additional code in standard Python.

(2) Course is better in the front end than in the back end.

(3) Week #6 is significantly more involved than previous weeks. Suggest divide Week 6 into two sessions: Neural Network and Nearest Neighbor applying neural network results (ImageNet 2012 was mentioned and not explained. Therefore the Nearest Neighbor homework assignment from the student's perspective does not have much to do with neural network other than using the results from ImageNet 2012, which was not explained in any detail anyway). This will allow more time to delve into the forward and backward propagation which should have been explained in more details.

(4) Home assignments are not best worded, especially homework assignment for Week 6. Suggest reword in shorter statements that are more to the point.

(5) Programming presentation and assignments can seem like exercise in graphlab and SFrame functions rather than machine learning.

PRO:

(1) Class presentation by Professor Fox on recommender system is detailed and clear.

(2) Classifier block diagram shown by Professor Guestrin is good, clearly distinguishing training the classifier and the subsequent use of the classification (prediction).

(3) Neural network quiz in Week 6 is excellent. It drills down on the multi-dimensional space that neural network is particularly good for.

By Herbert K

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Dec 9, 2015

Even though the course discusses relevant topics, the level is extremely low: The lab sessions were easily solved applying copy-paste code from the provided notebooks, with minor adaptions. Moreover, 8/10 questions in the lab sessions were not related to machine learning at all, but simply looping over data and counting or similar. The intro video and course introduction strongly suggested using deep learning in the course: did not happen. We trained k-means on pre-computed features which happened to come from from a deep learning network (not sure which one, inception? I didn't even watch the lectures here from disappointment). That is not deep learning, it just shows you how well deep learning can work.

Graphlab is a mature framework, I guess, but it's commercial and scikit-learn is better imho (and free!).

If you wish to learn machine learning, take the Stanford course on Machine Learning for Andrew Ng. This course is in MATLAB, not ideal for machine learning, but adequate for a better understanding of intelligent system implementations.

Maybe the course is OK if you're a beginner in machine learning, but not good.

By Rohan L

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Aug 29, 2020

I leave 2 stars as I learned a lot of new information and methods, and the theory and math behind them.

You will learn about Data Science and Machine Learning, but not much about Python.

The course is pretty much abandoned and outdated. Sframes and Turicreate packages (instructor's creations) are used instead of more universal packages. Installation in the beginning took some time and research. Many of the assignments have errors and bugs in the code that have not been updated. Forum assistance is abysmal for clarification or deeper questions. Many links are dead.

There are many times in the lectures where the instructors are writing several sentences in their handwriting on their notes instead of having the text ready to appear.

I would suggest using this course and series as a supplement to other information one as learned, not as an introduction for initial understanding. I found myself frustrated too many times.

By Mathew L

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Oct 27, 2015

Course doesn't do nearly enough to bring you up to speed on using mathlab or iPython notebook. I am currently learning to program python and a lot of this stuff was well above my head.

The quizzes and assignments do very little to reinforce the work, and often come down to trial and error. I wanted to learn the mechanics of machine learning from this course, but it is too complex, and presented in such an arcane manner to serve as an introduction, but doesn't go deep enough to really teach anything useful. I'd suggest you look at Wikipedia or YouTube for better classes.

I'd like to draw special attention to the quizzes, as often they're on trivia from the lectures and not reflective of the actual nuts and bolts of working with machine learning. They, as with the projects, I found to be a massive waste of my time.

By Peter G

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Mar 22, 2016

The teachers are easy to like, but the course content is very lightweight and will mostly teach you terminology with no real understanding.

The worst part was the assignments, which could all be solved by a little copy/paste: I didn't learn anything useful by doing them. All the actual algorithms were supplied in a separate module. More than that, many of the suggested solutions were bad coding (like collapsing 50% of the data before training, or writing sixteen special cases rather than a general function) or pointless (like training a linear classifier on pixel data).

There are better courses out there.

By Carlos K R

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Oct 3, 2016

Good course! The only major drawback is the requirement of Graphlab, which doesnt allow the student to fully understand the applications using real world software. Just recently, Dato (the company that owns graphlab) was purchased by Apple, and you can no longer buy a commercial licence to the software. Despite this, users cannot use Graphlab for commercial purposes, therefore rendering the software completely impractical for professionals. The specialization is designed to help you get a job (see capstone) yet the software currently in place is limiting.

By Valentin I C

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Apr 9, 2023

The library used for the course is unnecessary complicated to install and not compatible with windows or newer python versions. Alternative python libraries are suggested but there are no indications on how to obtain the same results neather is clear why certain results are different.

Considering the alternatives on the platform, the course itself is not so valuable to justify the necessar work to complete the assigment with another library or to learn to use the extremely unpractical one that is succested.

By Bruno C S d A

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Jul 15, 2016

I have no doubt teachers are excelent professionals in the area, as well as great machine learning enthusiasts. However, I did not like the fact that you get limited to learn how to use a paid and (very!) expensive platform, mostly because there are many other free packages available for machine learning. Ok, the platform offered makes things easier, but if you really want to learn machine learning, you can not be limited to a platform, acting as a robot just using pre-written functions in a black box.

By Simiao L

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Jan 3, 2016

2 stars because the theoretical part is ok but programming assignments are waste of time. I'm not here (and paid) to be trained to use something the instructor is trying to SELL, nor will I ever recommend this product for commercial use. I will switch to other "not recommended" packages in the later parts of this specialization.

They should put the disclaimer for Graphlab Create in the specialization page so people can be aware of this.

Besides, the sound of that Giraffe toy is really, really annoying.

By Giang H N

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Apr 10, 2021

Great content but the videos are severely outdated, don't match the given materials, certain quiz is incorrect due to the mismatch. It seems the course makers no longer have time to update the course because there have been discussion posts on these issues as far back as 8 months ago and things have not been resolved. Still worth going through if you already somewhat know the materials and can figure out the troubles on your own.

By Gary W M S

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May 20, 2023

The start of course was very interesting, I was following until the introduction of Mathematical equation, quadradic functions, additionally the inability to perform some the work due to no access to graphlab really become bothersome to the point, I have lost interest. I wish I could have continued, but due to the constraints, I will be bypassing this segment of my education. I'm sorry.

By Ira T

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Nov 1, 2015

It really just touches a lot on different machine learning techniques and really just sets the stage for the higher courses. Unfortunately some of the chapters (especially deep learning) are so brief that it is really frustrating trying to complete the quiz and assignment. Also the course doesn't use open source tools but a trial version of a pretty expensive library.

By Morten H

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Feb 8, 2016

Poorly executed. Constant differences in data. tiresome to watch two supposedly very intelligent instructors amuse themselves by saying Bro and Dude. The use og graphlab is unnecessary and adds a layer of complication which adds no future value to your toolkit. Probably a lot of better executed Machine Learning courses out there

By Tom L

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Jun 28, 2016

I like the case-based approach--this course gives a nice albeit shallow overview. I don't like that one professor uses this course to push his startup by asking students to use graphlab. A more commonly used library would have been a much better choice. Parts of the course feel like a "Getting started with Graphlab" tutorial.