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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
12,341 個評分
2,958 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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SZ
2016年12月19日

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.

PM
2019年8月18日

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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2801 - 机器学习基础:案例研究 的 2825 個評論(共 2,870 個)

創建者 Bruno C S d A

2016年7月15日

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.

創建者 Simiao L

2016年1月3日

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.

創建者 Giang H N

2021年4月10日

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.

創建者 Ira T

2015年11月1日

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.

創建者 Morten H

2016年2月8日

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

創建者 Tom L

2016年6月28日

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.

創建者 diego n

2015年12月18日

Having done some other machine learning MOOCS , this course seemed rather basic to me and did not enjoy too much using non open-source software for the programming assignments. The material is nice, In this sense, I would have expected to 'default' to sci-kit learn and offer using graphlab create as optional.

創建者 Advait S

2018年1月10日

While it was good for learning concepts I had real trouble with graphlab. Installation of graphlab never worked on my machine. I had to install VM just for being able to use graphlab. I really wish they had opted for more open source, free options or at least used ince such library along with graphlab.

創建者 Ziqian G

2020年8月11日

There are big problems in this course, like the installation process should be given in a more specific and vivid way so that I would not have spent three days on it being a windows user...(update: still can't access jupyter notebook after trying installing ubuntu, vmware workstation, filezilla).

創建者 Sunaad R

2018年7月30日

Too much dependency on Graphlab package is bad. If we are learning the concept, we should reduce the size of the sample data. We should be using generic open packages, so that our learning can be easily demonstrated anywhere (especially interviews), and not dependent on graphlab.

創建者 kunjan k

2015年11月5日

The case study approach is a great idea.

But I wish the instructors were more candid about the tools that were in use. It seems dodgy that the instructor is a CEO of a commercial tool vendor and is "encouraging" students to use it.

The quizzes in the course were extremely shallow.

創建者 Robert R

2021年5月5日

I believe these packages are out of date and the application side is not helpful.

The information on the theoretical side of things was extremely helpful to help build up my machine learning knowledge, but overall I don't feel like I'm taking away much from this course.

創建者 Raphael R

2016年3月19日

The overall quality of the course is good, but in my opinion the level is quite low and there is less content then I expected. The assignments are more or less copy-paste or very repetitive. The 5-8 hour work per week are a joke, I never needed more than 2.5h per week.

創建者 Matthew F

2019年7月21日

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.

創建者 Joseph J F

2017年8月20日

It is more a course in using the tools designed by the teachers than machine learning. It might do something for a less experienced user in programming, but I didn't find it much use. The overview of Machine Learning tasks isn't bad.

創建者 Andras H

2020年5月31日

on one hand good... on other hand annoying ( mixing graphlab and turicreate... shitty wording of the assignment task, info added as side note which was vital for the assignments...etc.) The curse material would need a refresh.

創建者 Sunil T

2020年5月24日

SFrame data do not support by an updated version of the Python, so student won't able to finish their assignments. So instructor need to update the materials and database which is supported by a new version of Python

創建者 Tudor S

2018年4月22日

The Assignments and Quiz questions are hard to read and comprehend.

Although individually the course presentations are ok, overall this course isn't a very relevant or coherent introduction to Machine Learning.

創建者 Taylor I

2020年5月11日

Feel like I have been duped in a way. No capstone project and you are pretty much forced to use Turi Create (proprietary/black-box version of pandas), which I found incredibly hard to install and use.

創建者 Ashley

2019年6月23日

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.

創建者 Arman A

2016年2月16日

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!

創建者 Annemarie S

2019年5月24日

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.

創建者 charan S

2017年7月16日

If someone is looking for ML foundations and what is ML, they can choose this course. This is very basic course and i feel should be excluded from the ML specialization.

創建者 Eiaki M

2016年3月5日

One would learn a thing or two, but the course is very sparse compared to other machine learning courses, and I didn't feel that it was worth the time and the cost.

創建者 Robert P M

2015年10月27日

I do not like this course being tied to a commercial product. In my opinion it should be using an open source python library and not focusing on the Dato product.