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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
8,760 個評分
2,094 個審閱

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....

熱門審閱

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

PM

Aug 19, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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1 - 机器学习基础:案例研究 的 25 個評論(共 2,017 個)

創建者 Ravi P

Sep 08, 2018

The materials used in this course are extremely outdated. In order to access the data to do the projects you have to use SFrame, which is only supported up to Python 3.4.x. Python is currently on v 3.7.0. The data should be provided as .txt or .csv to be more universal. The instructors claim that you don't have to use a specific library to do this course, but you have to have at least SFrame in order to access the data! Further I am sure SFrame and Graphlab are good tools, but the course should be taught with open source tools so that the students can continue to use those tools after the course is over.

I wanted to like this course. I did enjoy the professor's teaching styles, but the fact that I would have to download a new outdated python environment, and non universally accepted tools, to even access the data is a major deal breaker!

創建者 Oscar A R

Oct 24, 2018

I spend two days trying to get the graphlap lib working on two OS, and could not. I had to spend couple of hours setting up the aws services to be able to work with the samples.

Phd's I dont think they make good teachers....

Thanks.

創建者 Gianmaria M

Jan 22, 2019

Very relevant material clearly explained by the professors, who are very knowledgeable and engageing. However the installation and usage of the GraphLab module is cumbersome and plagued with bugs. This could still work if there was enough support however I did not find any helpfrom the mentors/tutors who simply did not answer my questions in the Forum thus making my experience even more frustrating. Pity, I certainly hope Coursera can fix it as the class is quite good

創建者 sreeraj c

Jan 21, 2018

Such a bad presentation with no help to people with graphiclab tool setup.

創建者 Sourav S

Jun 27, 2018

Too dependent on Sframes and graphlab which does not work most of the times. I had to spend an entire day just figuring out versions of python to make this work.

創建者 Jatin K P

Mar 28, 2018

To follow along the course you need to install Graphlab library, which is the biggest challenge. Also, the support you get from the creators are not good enough.

I regret to waste my time on this course.

創建者 Rahul D

Dec 23, 2017

Course uses proprietary packages. Better learning from "The Analytics Edge" conducted by MIT at Edx.org

創建者 Ernie M

Sep 25, 2017

I enrolled in this specialization to learn machine learning using GraphLab Create. Half way into the specialization the creators sold Turi, GrapLab's parent company, making it non available to the general public (not even by paying) and then all the knowledge devalued. I wish I had known this and I would have enrolled on a different specialization. The creators still give you the possibility of using numpy, scikit learn and pandas but I had already done a lot with GraphLab create. The time I invested on my nights after work became a waste. I was trying to convince the company I worked for to buy licenses for GraphLab create.

Coursera should not allow folks to create courses that promote a private license course because it would make people waste their time and money if they decide to privatize the software.

Don't take this course, and if you take it then only use GraphLab create when the authors give you no other option.

Teaching style: Carlos was good, Emily is not very clear and loses focus of the topics and often rambles. She seems very knowledgeable but she lacks clarity of exposition when compared to Carlos or Andrew Ng.

創建者 Elvin V

Oct 26, 2017

The worse course I have ever taken on Coursera. Forcing you to use their own library which is also not open source and free is ridiculous! You will never use graphlab in the future and there are better alternatives available! Totally useless experience. And most of the time vide lectures are just some mumbo jumbo, like showing diapers or napkins for 2 minutes! I have successfully wasted a lot of time on this course.

創建者 Walther A G L M

Jul 02, 2018

relying on proprietary library and unreliable notebook made this experience painful

創建者 Andrew W

Nov 04, 2017

Requires software called Graphlab Create that would not install on my machine. Unable to complete any of the course material due to this.

創建者 Ibrahim M A

Apr 29, 2017

My only happiest moment in this whole course is writing this review, I couldn't wait to finish it in order to give it the 1 star rating it deserved.

What I've seen from this course so far is abandonment , that's right this course is abandon ware, no questions get answered on the forums (asked a question a month ago and still didn't get an answer) and the links are outdated (links to further documentation don't work).

I wouldn't recommend this course to anyone wanting to learn Machine learning since the instructors use proprietary libraries that need a license to use outside this course thus application wise what you learn her isn't transferable only the conceptual content;however, even in that there isn't much content for, since everything is an introduction here so nothing is quite useful .

If your on a tight budget and your taking this specialization you could skip this course. Actually you could even skip this specialization since they canceled the capstone project so investing any money and time here is a waste. I can only recommend this specialization/course IF the instructors add a project at the end , be more involved on the forums , update non functional links ,and finally USE NON PROPRIETARY libraries hence they will need to take feedback from the students and redo most components of this specialization.

創建者 Theron R F P

Nov 05, 2017

Good intro to the ML concepts, but my review is negative due to :

創建者 Matthew H

Oct 26, 2017

I just completed the first week of this course and am choosing not to continue. The first week consisted of 75 minutes of video in which we learned a half dozen facts regarding Python syntax and the use of SFrames. This content could have been presented in a single 5 minute video with just a little planning and editing. I realize that the presenters perhaps wanted to ease folks in, but this is silly. There may be good content in the following weeks, but I am not patient enough to find out. Gonna try a different ML class. Sorry guys.

創建者 strx

Apr 12, 2017

Maybe a good course, but you need to be an IT crack to be able to install the software and make it works. Online help does not help. Irritating! 45USD lost. I don't recommend this course.

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 Susan L

Nov 05, 2018

Out of date. Should be retired or updated.

創建者 Eugene K

Feb 10, 2017

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework. Despite the good lectures, the assignments don't help you develop the knowledge required for ML developer role.

Taking in consideration the permanent postponing the courses delivery, from summer 2016 to summer 2017, finally the most interesting part of the specialization was cancelled. I'm completely disappointed with the specialization learning expirience.

創建者 Ron M

Sep 26, 2018

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.

創建者 Andreas

Jan 04, 2017

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

創建者 Pritish K

Apr 07, 2019

The most useless course on Coursera. I have wasted 3 weeks just trying to install Graphlab and the installation seems infinitely tedious. There is no support from Coursera or University of Michigan to install the software

Why do they insist on teaching on a software which have so many known issues and so many students are struggling to install the software.

The objective is to learn data analytics and machine learning, not to become a systetm admin and n IT guy.

創建者 Sharina C

Feb 09, 2019

I am extremely frustrated with this course. I have spent sooo many days just trying to get the software set up. It's currently week 3 and I can't complete week 1. I've followed the directions and run in numerous roadblocks, some of which I was able to resolve after searching through the course forums. I shouldn't have to scour the forums to get setup...the instructions should be updated. Unfortunately, I'm still stuck in week 1, unable to get the software running properly. It's really frustrating.

創建者 lianghui t

Mar 09, 2019

the graphlab can not be installed

創建者 Karthik M

Dec 27, 2018

A good course to understand the basics of Machine Learning. The only issue is the use of Graphlab library. Since it only works on Python 2.7, it is not convenient for people who prefer Python 3

創建者 Shibhikkiran D

Apr 13, 2019

This is course is very informative for a beginner. It helps you to get up and running quick provided you have little basics on Python. You should( sideline on your own interest) also pickup Statistics/Math concepts along each module to make a rewarding experience as you progress through this course.