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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
12,287 個評分
2,946 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
2016年10月16日

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
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.

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2701 - 机器学习基础:案例研究 的 2725 個評論(共 2,861 個)

創建者 Piyush K P

2016年10月24日

thanks to prof and cousera for this wonderful course. I wish the programming part was taught separately from basic. I have taken the previous course which was case study approach with respect to which it was slightly tough.

創建者 Jérôme B

2017年12月19日

The teachers are nice and the content is pretty interesting, but they keep talking about the Capstone that we actually won't do. That make me wonder if it's worth continuing, and wonder why they cancelled it eventually.

創建者 Gregory T

2016年10月30日

This was a valuable introductory survey course. For me, the challenge came from my unfamiliarity with Python not the material. I would rate this class as "entry level" for anybody with a college-level technical degree.

創建者 Brandon P

2018年3月10日

There were a lot of assumptions made about my math background. Terms and concepts were used that are foreign to most people and while the forums were helpful it was interesting to see that this is a common feeling.

創建者 Mohammad A

2019年7月22日

Course include great knowledge, but when coming to work on tools, they are using old method like we have python 3.7, but course is going through python 2.7 and also older version. That's creating confusion somehow

創建者 Ivan P

2016年5月6日

It's not a bad course, but it forces students to use GraphLab, a framework created by one the professors teaching the course, instead of using scikit-learn, a widely used framework for machine learning in Python.

創建者 chris s

2016年1月27日

This course has so much potential but is based on proprietary software. The instructors are excellent and the content is really good. It would get 5 stars if it was based on all open source software.

創建者 Nishant K

2020年10月31日

Great approach with basic explanation of applying and importance of the domain in read world examples. Could have been more in depth in few areas but hopefully will be taken care in following courses.

創建者 AHMED E A

2020年7月23日

The course needs to be updated....I have hard times installing turicreate and graphlab on my laptop... at the end, I had to use google collab....

I guess this course needs to use tensorflow instead...

創建者 Luis F A C

2020年12月5日

Aprendí muchas cosas interesantes. Actualmente es grande la dificultad para realizar las prácticas de programación con la librería que usan "graphlab" la cual no se relaciona my bien con windows.

創建者 Tom v S

2018年6月5日

In and of itself, the content of the course was pretty good. However, after working through 2 deep dive AI courses of each 6 months, obviously this particular course was not much of a challenge.

創建者 Diego A

2016年10月24日

The Professors and the lectures were excellent. Homeworks are way to easy. Would like to use open source tools like pandas and sci-kit learn instead of proprietary tools like graphlab.

創建者 Neelam

2020年5月18日

I cannot download all the software needed specifically Turicreate, despite the provided link it shows never-ending errors, after a week of trying I had to give up the course since.

創建者 Kenny J

2020年5月21日

This course needs to be updated. It's hard to follow the notebooks since the lecture was on GraphLab, and some of the explanations were not elaborate enough, especially Week 6.

創建者 Zein S

2018年1月17日

I like more to work with sklearn rather than GraphLab..

Actually many recommended this course to me, and I expect more excitement in the next courses in this specialization

創建者 Jonathan O

2021年4月14日

Pros : You will get a great fundamental conceptual understanding of basic ML concepts and practical implementations.

Cons: Using Turicreate over sci-kit learn and tensorflow

創建者 Eric.Wang

2016年3月10日

I don't like this course , because the homework can not match the lesson. I can not got more messages to completed the homework.

So I will Unregister this courser , Thanks.

創建者 Morteza M

2016年11月20日

The only reason that I am giving 3 star is the design of the quizzes for each week. The readings are too long and the content of the quiz sometimes gets you frustrated!

創建者 Chih W L

2016年9月19日

Professors are very good , i am really enjoy in this class, but no further discussion about implementing ML algorithm, just call the API to handle the sort of data.

創建者 Zhongyi T

2016年3月9日

The lectures are fine. However the content is way too easy. Another course on Coursera `Mining Massive DataSets` is much better, in the depth and horizon.

創建者 Fabio

2018年10月7日

App needed to complete assignments ceased to function early on - forum / admin did not help to find solution. Otherwise good intro to get started with ML.

創建者 Deleted A

2016年6月5日

Generally ok. Towards the end of the course, the lectures could have been a bit more in depth - or provide students with a more in depth reading list.

創建者 Kai W

2015年11月21日

I think this is an excellent course. I would have given 5 stars if this course is not based on Graphlab which is not affordable to the general public.

創建者 Murat O

2016年1月28日

Gives a really broad overview of ML concepts. Examples (and assignments) use a commercial Dato product called (GraphLab Create). Expect nothing else.

創建者 suresh k p

2018年7月28日

Nice explanation of basic ML but I would suggest please provide the practise tool with proper integration.That is a big headcahe in this course.