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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

10,629개의 평가
2,555개의 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


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.


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

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,471개 리뷰 중 2051~2075

교육 기관: Sunit K

May 31, 2020

Very practical course which gives you real world implementations of Machine Learning. Absolutely enjoyed the material!

교육 기관: Yub B

Apr 16, 2017

Pretty decent course for beginner to learn machine learning. A case study approach they used is great and interesting.

교육 기관: Neel S

Apr 30, 2020

It's very good and very well understandable course. It cover core concepts of machine learing.

Thank You! Sir and Mam

교육 기관: Peter W

Mar 11, 2016

In general a good course, well presented.

4 start only due to some assignment questions not being covered in lectures

교육 기관: Kevin A

Jun 29, 2017

I haven't yet finish this course but is a excellent introduction for begin to study in this computer science field

교육 기관: ANKIT S

Jul 05, 2020

i would rather give 5 but as i wasted lot of time due to graphlab so i have take one star for that my satisfaction

교육 기관: Sarra Z

Jan 22, 2020

I liked this course, it is really based on study cases approaches and covers many problematics in machine learning

교육 기관: Ankit T

Aug 29, 2019

The course provided by Course era was really very good. I want to thank to Course era to give me this opportunity.

교육 기관: golap h

Jul 02, 2020

This machine learning course is really effective for beginners.I have learned many basic topics from this course.

교육 기관: weiyuan x

Apr 12, 2017

Good start course. It seems some information not covered so sometimes it is difficult to understand the content.

교육 기관: Christophe M

Nov 08, 2015

Very didatic approach to machine learning. Easy access and still powerful technique to understand how it works.

교육 기관: Bhaargavi A

May 08, 2018

Good Course. Teachers have taught it well and the jupyter notebooks are good and give a good deal of practise.

교육 기관: Soham K

May 28, 2020

Overall it has been a great experience. But in my opinion, the course videos should be updated to TuriCreate.

교육 기관: Kuldeep K

May 11, 2020

Kindly change the GraphLab package system, the majority of the compiler doesn't support this.

Else it was good

교육 기관: Azhar B T

Sep 16, 2019

the course is good but rely on graphlab and lack of hands on with python is the reason i cannot give 5 stars.

교육 기관: Philip L

Nov 13, 2016

Some quiz questions' answers are incorrect and instructors need to update the quit to reflect correct answers

교육 기관: Islam M

Apr 19, 2016

-great introduction .

-go through a lot of exciting topics.

-but the implementation part is boring something.

교육 기관: M.sakif m

Nov 16, 2015

Interesting class, but should have used open source python libraries instead of restricted license libraries.

교육 기관: MOHD S M

Sep 15, 2019

Amazing they guide me help .Special sir working on project .It clear my concept with the real world example.

교육 기관: Vasudev V

Feb 20, 2017

It would be great if you could intersperse theory and practical sessions. Otherwise, a very useful course...

교육 기관: Mohit P

Mar 12, 2019

This course is a great starting point who has no earlier experience of ML. . Cheers to the course makers!!!

교육 기관: Yuting S

Feb 26, 2019

Wonderful course.

The only problem is that I can't review the course materials after completing the course.

교육 기관: C K S

May 28, 2018

Course was nice and especially special thanks to both the faculty's who make us to understand the course.

교육 기관: Benson

Sep 04, 2017

Interesting, I never used graphlab before. It would be better if this course went through algorithm deeper.

교육 기관: Javier M

Jul 17, 2017

Great introduction to the topic. I think the juniper notebook is still buggy. Its stability can be improved