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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
12,649개의 평가
3,028개의 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

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.

BL
2016년 10월 16일

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

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,947개 리뷰 중 2676~2700

교육 기관: Le H P

2019년 8월 16일

well done!

교육 기관: Daniel Ø

2016년 1월 18일

very basic

교육 기관: Muhammad A K

2020년 11월 27일

very good

교육 기관: Sayam N

2020년 9월 25일

Excellent

교육 기관: Aishwarya S

2020년 7월 5일

very nice

교육 기관: Zhen W

2017년 7월 5일

Good ~~~~

교육 기관: Kevin C N

2016년 12월 10일

Thanks!!!

교육 기관: Oriol P

2016년 3월 30일

Was nice!

교육 기관: Sreemannarayana B

2016년 2월 23일

Excellent

교육 기관: Oumar D

2016년 2월 21일

Efficient

교육 기관: DEBASISH M

2020년 9월 21일

Like it.

교육 기관: John M

2018년 7월 4일

Liked it

교육 기관: Phoenine

2018년 12월 23일

So good

교육 기관: SHISHANTH R

2021년 9월 6일

good

교육 기관: Deleted A

2020년 8월 14일

good

교육 기관: YEDURADA J K

2020년 8월 10일

nice

교육 기관: Rohan B R

2020년 6월 24일

nice

교육 기관: Lucky V

2020년 6월 21일

cool

교육 기관: Dr. A S M M R

2020년 6월 6일

Good

교육 기관: 楊傑綸

2015년 12월 29일

Cool

교육 기관: 王博

2015년 11월 13일

nice

교육 기관: Brijmohan S

2018년 3월 22일

V

교육 기관: Sofia P

2016년 3월 12일

I did not have a lot of experience in machine learning, so this course was very good in the aspect of introducing people to machine learning concepts. Most of the times the material was very well explained, and I like the concept of the tutor writing on the screen at the same time they are presenting, personally it helps me more. Some of the quizzes were easy so you did not need a lot of preparation, some of them were more difficult or troublesome, like the quiz for Deep Learning. I also liked the graphlab module, I think that learning how to use it will help me with my own work.

However, as this course does not really go in depth in the algorithms themselves, I feel that after one month and a half I have a basic idea, but I haven't learned much about how to implement machine learning on my own even in basic things, while other courses have more or less the same time frame and are more dense in their material. In my opinion, this whole introductory course would just be just splitted and each of these intrductory weeks would be appended as the first week of the subsequent modules to come. Because anyways, after 4 months in the specialization, if somebody continues to the recommender systems module for example, he/she would have forgotten the basics of this so they would need to cover again the recommender systems week in this course. And from the other hand, if some introduction is again repeated in the subsequent modules, then why have this introductory course anyways?

Thanks.

교육 기관: Denys G

2016년 1월 13일

The biggest downside of the course is that instead of learning on open source machine learning modules (sklearn) the course offers Dato's GraphLab, a proprietary piece of software that requires paid licenses to operate.

To be clear, during the duration of the course students can use a student license that provides graphlab for free but this expires after a year. It seems like fine software but if you arent going to purchase a license after the class expires whats the point? Also, Graphlab is built on top of python2.7. If you are running python 3.0+ on your machine youll have to install a python 2.7 instance.

Otherwise the quality is solid. The philosophical approach the professors take is to give you a taste of a variety of machine learning models. The upside is that if you want to get a taste you can. The downside the course feels pretty shallow and then the next course in the specialization -- regression -- feels like a pretty stark contrast. In general it could be argued that this is a problem with all coursera courses. How do you modulate course difficulty when you could be targeting students who are somewhere between high school kids to computer scientists? So the course and the specialization tilts between very easy and very hard.

교육 기관: Steven D

2016년 9월 11일

The course is effectively a tutorial on how to use proprietary software to solve a range of machine learning problems.

I liked the fact that the course covered a wide range of problems quickly. There were however two issues that I did not like.

1) It is not well supported and given that the technology is proprietary, there are few other places that offer support (i.e. you can’t just look at problems and solutions on stackoverflow to get insight into the tech)

2) For a course labelled as “intermediate”, it presented very little detail. Most of the course was dedicated to explaining particular problems, the solution to which was inevitably “then you train this really clever, one-line algorithm we have written for you and you query it for insights”. I felt a little cheated by this approach to a subject which should be really fascinating.

While some of my concerns may be addressed in follow on courses, I am left with little insight into what really lies ahead. For example, is this really an “intermediate” course? What background do I really need? Will we ever get to the detail or will I always just be expected to call someone else’s brilliant algorithm and accept the result.