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Machine Learning Foundations: A Case Study Approach(으)로 돌아가기

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

4.6
별점
12,728개의 평가
3,039개의 리뷰

강좌 소개

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

SZ
2016년 12월 19일

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
2019년 8월 18일

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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,963개 리뷰 중 2776~2800

교육 기관: Piyush K P

2016년 10월 24일

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.

교육 기관: Jerome B

2017년 12월 19일

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.

교육 기관: Gregory T

2016년 10월 30일

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.

교육 기관: Brandon P

2018년 3월 10일

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.

교육 기관: Mohammad A

2019년 7월 22일

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

교육 기관: Ivan P

2016년 5월 6일

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.

교육 기관: chris s

2016년 1월 27일

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.

교육 기관: Nishant K

2020년 10월 31일

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.

교육 기관: AHMED E A

2020년 7월 23일

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...

교육 기관: Luis F A C

2020년 12월 5일

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.

교육 기관: Tom v S

2018년 6월 5일

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.

교육 기관: Diego A

2016년 10월 24일

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.

교육 기관: Neelam

2020년 5월 18일

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.

교육 기관: Kenny J

2020년 5월 21일

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.

교육 기관: Zein S

2018년 1월 17일

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

교육 기관: Jonathan O

2021년 4월 14일

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

교육 기관: Eric.Wang

2016년 3월 10일

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.

교육 기관: Morteza M

2016년 11월 20일

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!

교육 기관: Chih W L

2016년 9월 19일

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.

교육 기관: Zhongyi T

2016년 3월 9일

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.

교육 기관: Fabio

2018년 10월 7일

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.

교육 기관: Deleted A

2016년 6월 5일

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.

교육 기관: Kai W

2015년 11월 21일

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.

교육 기관: Murat O

2016년 1월 28일

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

교육 기관: suresh k p

2018년 7월 28일

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.