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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

11,429개의 평가
2,734개의 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


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.


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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,655개 리뷰 중 2076~2100

교육 기관: Sunil K S

May 19, 2020

The course was very informative but I face a lot of problems in installing Graphlab and Turicreate. I request the Mentors please use the Pandas data frame in place of SFrame. The mentors are cool.

교육 기관: Hanz C V

May 22, 2016

Good for a introductory course if someone is getting started with machine learning, but as part of an specialization i think is useless (for people who are planning to take all the specialization).

교육 기관: Jayakrishnan M M

May 26, 2020

Graphlab is used during the class, where as in assignments, turicreate is used. This causes slight variation in the results between the two. This may cause loss of points in the assignment.

교육 기관: Najamuddin B

Jun 01, 2017

Course contents are good - however the forums are not active and there is no follow up from faculty to update the course specialization following the change in course structure (eg. no capstone)

교육 기관: Krzysztof L

Aug 14, 2016

This course is very good. The only problem is that instead of using open source packages like scikit-learn they decided to based it on proprietary GraphLab (which is free only for academic use).

교육 기관: Matej M

Nov 17, 2017

Good course, a tiles a litte coursory, but a decent introduction to the concepts and vocabulary of machine learning. Something like this should be required for anyone who works with data today.

교육 기관: Rakesh

Jun 13, 2016

Decent intro, though it would be a lot more useful if the professor didn't use his Software and instead thought us implementations using python/R which are used in most commercial applciations

교육 기관: George G

Sep 24, 2018

It gives you a fair insight to the world of machine learning, without getting into much technical detail. I guess this information is saved for the next courses of the Machine Learning group.

교육 기관: sandeep d

Aug 17, 2020

it would be really great if you will teach the provided note book practice examples

and deep learning is a bit harder and faster

instead graphlab if you use sklearn module it would be amazing

교육 기관: Yu Z

Jun 18, 2016

This course provides a quick and easy introduction to machine learning and python. I enjoy the learning experience. The materials have room for improvement: there are typos and redundancies.

교육 기관: Tahsin T

May 31, 2020

It is a good course indeed. I have enjoyed the notebook practical part most. Though the theoretical part is a bit boring, I have learnt a lot. Thank You for designing the course in such way

교육 기관: Dominic

Sep 17, 2017

I like the introductory format of using case studies of a wide range of methods, it gives you an overview of the core machine learning algorithms that are used, and what they are used for.

교육 기관: Santiago J G C

Jul 06, 2020

Se deben actualizar algunos Notebooks, la librería de turicreate ha cambiado y algunas funcionalidades no están disponibles para python 3. Lo cual complica las respuestas en los examenes.

교육 기관: Keng-Hui W

Jul 15, 2016

Many practical examples for usages of machine learning.

Almost concepts, no hard math works.

Recommend for beginners who interested in machine learning but did not have any math background.

교육 기관: Frederick R B

Apr 07, 2016

The course is fantastic and presented well. I never got my feet under me because i had a lot going on at work. Does have some linear algebra pre-reqs that you can brush up @ khan acedemy.

교육 기관: Mridul C

Jul 08, 2020

Every topic is nicely explained in this course but the only problem is that I was unable to install graphlab library, so it would've been better if any other library would've been used.

교육 기관: Manuel T F

Apr 13, 2017

It is a great course. Congratulations! Everything is subject of improvement, though. Check again that the version of graphlab referred to in the videos is the one available to download.

교육 기관: Sharma K

Oct 31, 2015

The instructors are excellent and the material is good. The only drawback is the need to use Graphlab. This would have been a really great course if we had to use open source software.

교육 기관: Chandan K

Oct 09, 2019

Wonderful Course that I have really enjoyed while learning understandings of Machine Learning and its applications in real-world problems. Thanks to Instructors and Coursera and Team

교육 기관: Joshua R A P

Aug 19, 2017

i understand the use of graphlab to make thinks easy... but it would be better to state that on the description of the course, because i don't see myself using graphlab in the future

교육 기관: Ahmy Y

Nov 01, 2015

Great introduction course on Machine Learning. Got hands on experience to build Regression, classification, clustering, recommending & deep learning model with python and Graphlab.

교육 기관: Ornella G

Feb 29, 2016

I really like this course. Very nice introduction to ML. The only downside is that it would be nice to have some comments on how to implement the algorithms without using Graphlab

교육 기관: Stanislav K

May 22, 2017

Very interesting and easy to learn course. But I'd like to more of mathematical background in algorythms and of course use open-source libraries instead of proprietary graphlab.

교육 기관: Subashish S

Mar 25, 2016

Very nicely compiled set of lectures: I am new to machine learning and am finding this course very conducive to understanding some key ML algorithms using a case-study approach.

교육 기관: Gagandeep S

Nov 01, 2019

The case study approach of this course makes it interesting and fun to learn the concepts of Machine Learning. I only wish more live examples were used to explain the concepts.