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워싱턴 대학교의 Machine Learning Foundations: A Case Study Approach 학습자 리뷰 및 피드백

4.6
8,958개의 평가
2,143개의 리뷰

강좌 소개

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....

최상위 리뷰

BL

Oct 17, 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

SZ

Dec 20, 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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,062개 리뷰 중 2026~2050

교육 기관: Bruno C S d A

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.

교육 기관: tiafvoonug k x

Jan 06, 2016

As a non programer, or mathematician, this course is too hard to follow.

교육 기관: Arman A

Feb 16, 2016

The course uses proprietary tools for machine learning and data manipulation, making it effectively useless! However, the material on describing the machine learning algorithms were excellent!

교육 기관: David F

Dec 02, 2015

I didn't like the python environment, I thought it will be more like Ng's course. Nice explanations, but for amateurs.

교육 기관: Herbert K

Dec 09, 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.

교육 기관: Mathew L

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.

교육 기관: Satyam N

Mar 26, 2018

This course doesn't give any insight about the algorithms.

교육 기관: Mark F

Dec 19, 2015

This course is to much about graphlab and not enough about the mechanics of machine learning.

교육 기관: Piotr T

Oct 06, 2015

it's rather a course on using API of proprietary software with very very basic background on the actual math underneath

교육 기관: Ira T

Nov 01, 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.

교육 기관: Simiao L

Jan 03, 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.

교육 기관: Morten H

Feb 08, 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

교육 기관: Daniel J

Jan 07, 2017

excessive use of GraphLab create which is not an industry standard.

교육 기관: Jean T

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.

교육 기관: Peter G

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.

교육 기관: Najmeh R

Oct 04, 2016

The subjectes are not learnt deeply and precisely. Too summarized and vague!

교육 기관: Andrew S

Dec 03, 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.

교육 기관: Arun J

Sep 18, 2018

not useful since the material covered lacks any rigor.

교육 기관: Ashley

Jun 24, 2019

Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.

교육 기관: Annemarie S

May 24, 2019

The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.

교육 기관: Matthew F

Jul 22, 2019

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

교육 기관: Krupesh A

Feb 15, 2019

Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.

교육 기관: Kaushik M

May 01, 2016

Too many videos and not cluttered assignment codes

교육 기관: Eduardo R R

Sep 23, 2015

This course rely on commercial library. I am sorry, I don't believe the convenience of a commercial library is good for your learning. You may end up locked in.

교육 기관: Phillip B

Sep 25, 2015

Would have greatly preferred if open source tools were used.