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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
9,291개의 평가
2,218개의 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

Dec 20, 2016

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.

BL

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 2,139개 리뷰 중 2101~2125

교육 기관: Joseph J F

Aug 20, 2017

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.

교육 기관: Vakkalagadda A r

Dec 28, 2015

Instructors or TAs are not available in discussion forums. and the course is focussed on promoting "graph lab" proprietary package of the course sponsor. Maybe you can have a look, not beneficial if you are serious about learning ML.

교육 기관: Tudor S

Apr 22, 2018

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.

교육 기관: Ashley

Jun 24, 2019

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.

교육 기관: Arman A

Feb 16, 2016

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!

교육 기관: Annemarie S

May 24, 2019

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.

교육 기관: charan S

Jul 16, 2017

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.

교육 기관: Joseph C

Jul 29, 2018

Overly relies on a paid software (free for the course) called GraphLab. The course can be completed without GraphLab, but expect little / no responses to questions.

교육 기관: Eiakihonroeda M

Mar 05, 2016

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.

교육 기관: Robert P M

Oct 27, 2015

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.

교육 기관: Evlampi H

Nov 05, 2015

The framework is ok, but it would be more insight on the functions would be much more amplifying the learning process.

Good working examples, though!

교육 기관: Piotr T

Oct 06, 2015

it's rather a course on using API of proprietary software with very very basic background on the actual math underneath

교육 기관: David F

Dec 02, 2015

I didn't like the python environment, I thought it will be more like Ng's course. Nice explanations, but for amateurs.

교육 기관: Patryk H

Oct 14, 2015

Due to many technical issues with GraphLab lib I have to reduce acitivity in this curse for only video viewing :(.

교육 기관: David H

Oct 31, 2015

Very, very high altitude introduction presented in a seemingly confused way with a lot of product placement.

교육 기관: Zuozhi W

Feb 08, 2017

TBH this class's experience is not good. The lecturers seem unprepared and they talk very repetitively.

교육 기관: ashish s g

Feb 15, 2017

Very good course material. However, Graphlab is no longer free to use for commercial purpose.

교육 기관: Mark F

Dec 19, 2015

This course is to much about graphlab and not enough about the mechanics of machine learning.

교육 기관: Najmeh R

Oct 04, 2016

The subjectes are not learnt deeply and precisely. Too summarized and vague!

교육 기관: tiafvoonug k x

Jan 06, 2016

As a non programer, or mathematician, this course is too hard to follow.

교육 기관: Daniel J

Jan 07, 2017

excessive use of GraphLab create which is not an industry standard.

교육 기관: Satyam N

Mar 26, 2018

This course doesn't give any insight about the algorithms.

교육 기관: Keith P D C

Oct 28, 2019

Two stars because of GraphLab! Otherwise great concepts!

교육 기관: Ivo R

Nov 22, 2019

This course is very frustrating because it uses a library called Turi Create that can't be installed on Windows 10. There is no support on how to setup you local environment after three days of frustration I decided to cancel my subscription.

When I opened the forum for week one all the threads were asking the same question: "How to install Turi Create on Windows 10."

It would have been much better if the course was done with a more popular library like Skit-learn.

This course is useless if you don't use a Linux or a Mac

교육 기관: Yipeng H

Feb 24, 2020

This is the most junk and worst course I have ever taken. It has been so many years, and the software recommended by the two doctors cannot be installed at all. Now the most popular numpy and pandas are not mentioned at all in the course. All the videos are related to the junk-like software. I don't know why such quality courses can still be put on the coursera platform.