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

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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


2016년 10월 16일

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


2019년 8월 18일

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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 3,063개 리뷰 중 26~50

교육 기관: Ron M

2018년 9월 26일

I signed up for this course and began the reading and videos, but once it was time to begin interacting with the technology required (Amazon Web Services) , it appears this course is not longer supported by the instructors. Most communication on the course seems to have stopped between 1 and 2 years ago. Recent comments on the discussion forum no longer receive a response.

교육 기관: Yaron K

2016년 7월 13일

The Lecturers are very enthusiastic, but I was hoping for examples and assignments based on Pandas and Skikit-Learn. Instead the course examples and assignments are based on a machine learning package called Graphlab, that stopped working when it was upgraded to version 2 (there are workarounds that enable it to work locally, but clearly it isn't "enterprise ready")

교육 기관: Charlotte E

2016년 4월 12일

I feel like it should have been mentioned a lot clearer before starting that this was simply a course in how to use the creators library. These skills are not transferable anywhere else as I would have to pay to use them in future! Would have been a lot more useful as a how to for sci-kit and pandas.

교육 기관: Sam Z

2016년 12월 20일

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.

교육 기관: Florian M H

2020년 5월 12일

I am a professional SW developer (Embedded C for control units). I do not recommend this course for people who already know something about machine learning. If you want to learn the basics of ML, Stanford's Machine Learning course is a far better choice (is based on Matlab though).

This one here has far too little content.

Moreover, in case you cannot install the needed GraphLab/TuriCreate SW package (only MacOS or Unix, for Windows not always working, as for me also!) then you're basically left alone with finding a) the SW packages you need (I took scikit, numpy, pandas) and the corresponding commands (because the entire course explains ONLY commands for Graphlab, NOT for the other packages) - this is BIG extra work you need to do on your own. Now the big joke is that all other courses in the specialization are NOT based on Graphlab, but on the other packages I mentioned ;).

In addition: Literally 0 support from teachers/mentors in the forum during the course. The students have/had to handle most/all problems themselves. This is a no-go.

교육 기관: Susan L

2018년 11월 5일

Out of date. Should be retired or updated.

교육 기관: Andreas

2017년 1월 4일

This specialization is delayed for months now - very annoying! Don't give them money!

교육 기관: Iori N

2016년 1월 26일

i cannot spend $4000 per year package just to learn this course. sorry i am off...

교육 기관: Sarah S

2016년 2월 13일

Unsufficient information for the programming assignments.

교육 기관: Ken C

2017년 2월 4일

Not happy about course 5 & 6 got cancelled.

교육 기관: Brett L

2016년 10월 17일

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

교육 기관: Hugo N M

2016년 2월 7일

The course has a fundamental problem, it relies completely on a library developed by one of the instructors, which is not open source. In the end, it seems like a big opportunity of delivering a marketing campaign by the instructors then otherwise.

I definitely will not spend time and money on the other courses of this specialization.

교육 기관: Nils W

2019년 9월 19일

The course could be great, if it won´t depend ob Python 2.7 and graphlabs (because scikit isn´t scalable). Also some quiz questions are so hard, that it is impossible to answer only with the material. So they use forum posts to answer how you can find a solution to the quizzez. So in total more a waste of time.

교육 기관: john p

2016년 5월 13일

No Open Source Libraries, this course is not educational; it is a sales pitch to use their expensive software. Good luck having an employer pay this amount of money for software when they can hire employees that can use free open source libraries.

교육 기관: Christopher W

2015년 10월 15일

The fact that the class uses GraphLab instead of pandas/numpy/sklearn should have been stated up front

The course felt like an advertisement for the professor's toolkit

It was very disappointing that the equivalent standard workflow was not supported

교육 기관: Miro F

2020년 3월 14일

The instructors need to specify that you can run this course specialization using MAC or Linux only. I have wasted my time for the past 3 weeks trying to figure out how to run the Sframe or Turi using windows and could not find any solution.

교육 기관: Natalia Q C

2019년 7월 24일

The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that! I want MY MONEY BACK!!!

교육 기관: Alejandro

2016년 6월 13일

Shame that it was not possible to progress with this course without using graphlab which the creator of this course himself created. Please see the course as just a training sales promotion for his ML application.

교육 기관: Pooja M

2019년 8월 19일

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.

교육 기관: Anton M

2018년 2월 12일

Very interesting course, many thanks to Emily and Carlos.

The approach in explaining materials was exactly what I was looking for in order to understand both applications and implementation of AI.

교육 기관: Wei-Zhe Y

2019년 3월 18일



另外可能是在下才疏學淺搞錯了,在一些linear regression或是logistic regression的範例中,由於案例中的dummy variable過多,造成變數之間線性相依(n維空間中有k組向量,若k > n,必然存在若干向量彼此線性相依),直覺上有無數組解都可以達到幾近0的SSE,因此縱使結果再漂亮,對那幾個case中的參數,個人其實感到相當的疑惑。類似的困惑還有推薦系統的上課實例等。


교육 기관: Mohamed E

2020년 6월 6일

the course concepts were good but as everyone is saying the materials are outdated and you use TuriCreate instead of GraphLab so you have to search for the appropriates functions some times, and the installation was hard too because TuriCreate works only on Linux or WSL, I almost quit the course because I couldn't install it at first

교육 기관: Tomas R L

2021년 10월 1일

This was a really nice course, clear and introductory, a nice way to get introduced Machine Learning. I did this course cause I had free from my university, but I wouldn't recommend paying for it. The course is abandoned and a little outdated, so don't expect more than just an introduction

교육 기관: Igor K

2016년 6월 18일

I can only infer that this course's target audience is rich pregnant women who care about shoe shopping and celebrities. Unfortunately I am none of those things and had to cringe my way through the examples, watching the videos at 2x speed.

The course itself is incredibly shallow, even for a survey course, and basically serves as an ad for one of the professors' own products -- Graphlab Create. You'll be much better off taking Andrew Ng's course, which is significantly more in depth and forces you to write your own solutions to problems instead of relying on a proprietary library.

The only reason to prefer this course is if you really dislike the idea of using matlab.

교육 기관: Hector G A ( O - T ( A

2022년 1월 22일

Dear All,

Do not take this course. The material is interesting but the course is old. Currently, there are compatibility issues between python and turicreate. You may get stuck in the first week. I have asked support to Coursera and what they will do is to prompt you to a website suggestions that do not work either. I have already highlighted this issue to courser (either they update the content or remove the course from the list). I strongly suggest to find another similar courses with more recent content.