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返回到 机器学习基础:案例研究

學生對 华盛顿大学 提供的 机器学习基础:案例研究 的評價和反饋

4.6
10,989 個評分
2,635 條評論

課程概述

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....

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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.

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26 - 机器学习基础:案例研究 的 50 個評論(共 2,552 個)

創建者 Yaron K

Jul 13, 2016

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")

創建者 Charlotte E

Apr 12, 2016

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.

創建者 Ivo R

Nov 22, 2019

This course is very frustrating because it uses a library called Turi Create that can't be installed on Windows 10. There is no support on how to setup you local environment after three days of frustration I decided to cancel my subscription.

When I opened the forum for week one all the threads were asking the same question: "How to install Turi Create on Windows 10."

It would have been much better if the course was done with a more popular library like Skit-learn.

This course is useless if you don't use a Linux or a Mac

創建者 Andreas

Jan 04, 2017

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

創建者 Iori N

Jan 26, 2016

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

創建者 Sarah S

Feb 13, 2016

Unsufficient information for the programming assignments.

創建者 Susan L

Nov 05, 2018

Out of date. Should be retired or updated.

創建者 Ken C

Feb 04, 2017

Not happy about course 5 & 6 got cancelled.

創建者 Wei-Zhe Y

Mar 18, 2019

在上這門課之前,其實我就具備了這堂課大多數內容所需的知識,包含這些模型的方法以及數學證明等,因此這門課對我的幫助在於graphlab的使用、各種案例的探討及實踐。

由於有一些先備知識,這門課程的部分案例及題目,是我覺得不太能接受的,例如說:雖然課程中有提到overfitting觀念,但很多題目看起來都只在表達參數越多效果越好。

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。

課程主要專注在案例分享及各種方法的簡介,整體順序安排相當不錯,兩位講師的描述也相當生動有趣,有很多地方讓人感到耳目一新、獲益良多。不過關於模型的限制覺得還需要更多的解釋,才不會讓人誤用了一些不恰當的方法。

創建者 Hugo N M

Feb 07, 2016

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.

創建者 Sam Z

Dec 20, 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.

創建者 Pooja M

Aug 19, 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.

創建者 Igor K

Jun 18, 2016

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.

創建者 Nafi A K

Oct 15, 2017

the course contains misleading information about a capstone project that I discovered -by coincidence - that is no longer exists, the video introduction and the final videos is mentioning the capstone project time and again ! , I think this is a major problem bacause such project was one of the most usefull demonstration of the skills that one could acquire from the course, if I knew this before I would not have enrolled in this course, unfortunatly I discovered this when I am already in the second Regression course!

創建者 Dmitry V

Apr 01, 2016

I'm sorry, but this is just ridiculous. I can't recommend this course to anyone. It's all about advertizing: Emily Fox can't stop but recommend Amazon services, and Carlos Guestrin does the same for his Dato's Graphlab Create, which is might be great in general, but absolutely useless in educational purposes. The practice part of every week is just a waste of the time.

I can't say "money well spent".

創建者 郑轶松

Dec 27, 2015

LIKE an advertisment!

Why not use pandas and numpy sk-learn?

Open source is more popular!

創建者 Arun J

Sep 18, 2018

not useful since the material covered lacks any rigor.

創建者 Mike L

Sep 07, 2016

Might be good for someone looking for a casual overview?

I really wanted to like this course, and was excited about the series. Very disappointing. Refunded after scoring 100% on first three weeks and watching the theory portion of week 4. I was familiar, with the subject prior to taking this course; was hoping for a deep dive.

Too many trivially short and low information density videos. Handwavy mathematics. I would have liked to get a more solid idea of the depth of the series from the first course before committing money.

Default software for the course has near-zero market penetration (per indeed.com), unless maybe you work at Apple -- not really excusable for something that purports real-world value. Yes, you can use other software, *except for the capstone*, per another reviewer: this is fatal.

Presenters just not fully prepared to lecture on the topic: the nail in the coffin was the end of the week 4 lectures on clustering: "So, at this point, you really should be able to go out there and build a really cool retrieval system for doing news article retrieval. Or any other really, really, really cool retrieval that I can't think of right now. But of course there's lots of interesting examples. So go out there and think of ideas that I can't think of right now." Really? How about: "Cut! Take two."

Many poor design choices for the presentation. Too much time spent writing things on slides that should have already been on the slides.

As of this review: no reviews on the last courses in the series, and some poor (but indicative) reviews of the other courses.

創建者 Florian M H

May 13, 2020

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.

創建者 Jakub A

Mar 16, 2020

Definitely too little detail, too little math - for people with academic background this course may be confusing and, ironically, hard to understand because it tries to be "intuitive" - omitting the important details and formalities, in other words. The biggest downside is the TuriCreate library - it's not well known, uses other syntax than popular libraries like Pandas or Scikit-learn for some strange reason, and does not have impact when written on CV. I've known about 3/4 of the course beforehand and it was both not good for recalling the prior knowledge and for learning new things (I don't feel I understand anything new from this course). A big letdown overall.

創建者 Yipeng H

Feb 24, 2020

This is the most junk and worst course I have ever taken. It has been so many years, and the software recommended by the two doctors cannot be installed at all. Now the most popular numpy and pandas are not mentioned at all in the course. All the videos are related to the junk-like software. I don't know why such quality courses can still be put on the coursera platform.

創建者 Nils W

Sep 19, 2019

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.

創建者 john p

May 13, 2016

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.

創建者 Christopher W

Oct 15, 2015

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

創建者 Amirhossein f

Mar 14, 2020

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.