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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

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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


2019년 8월 18일

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.


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 3,043개 리뷰 중 2876~2900

교육 기관: Yuliana F N

2020년 12월 22일

Me pareció algo confusa la explicación de los modelos de recomendación, creo que debió ser más clara y y práctica.

교육 기관: Ajay S

2019년 3월 4일

Good for beginner level, not for intermediate or advance level. I learned more about graphlab than anything else.

교육 기관: Serban C S

2018년 2월 11일

Using a proprietary library for a paid course is not really a big issue but some people will be turned off by it.

교육 기관: Pēteris K

2017년 9월 23일

Definitely a good intro to the richness of ML, but would have preferred more rigorous assignments and evaluation.

교육 기관: Luca

2016년 11월 10일

not using scikit and assigment way too easy, not challenging, but high quality video, very easy to understand .

교육 기관: Pubudu W

2017년 7월 10일

Good survey course on ML techniques. Not very detailed and the exercises are too simplistic for real learning.

교육 기관: Nguyễn T T

2015년 10월 13일

the lectures are pretty great, engaging. the assignments stick with the lab exercise. the forum pretty active.

교육 기관: ADNAN A G

2020년 10월 9일

old and bad quality but very good explanation half of the course is programming there is no machine learning.

교육 기관: Nebiyou T

2017년 6월 7일

Some of the modules lacked polish and have not been updated since initial recording!

But they were practical.

교육 기관: Thomas M G

2018년 2월 21일

In my view, too much focus on GraphLab.

This is a problem because GraphLab doesn't seem to be open source.

교육 기관: Zizhen W

2016년 10월 16일

Some instructions of the programming assignments are not all that clear, which wasted me a lot of time.

교육 기관: Rajdeep G

2020년 9월 7일

They should upgrade the course in respect to python 3. Irrespective of that the theory part was great

교육 기관: Tilo L

2022년 5월 20일

I​ntresting topics get broadly introduced, sadly the course it outdated at a number of occasions...

교육 기관: adam h

2016년 2월 8일

would vastly prefer if this was taught using sckit-learn and pandas, given their broader use.

교육 기관: Reem N

2022년 6월 23일

It is very general however it gave me an insight to different machine learning applications.

교육 기관: Cameron B

2016년 4월 20일

The course is ok, the instruction was very poor for the deep learning section of the course.

교육 기관: Uday K

2017년 5월 1일

The theories for the models should be explained in more detail and with few more examples.

교육 기관: Alexander B

2015년 11월 4일

lectures were well done, but the strong focus on using graphlab ruined this course for me

교육 기관: Naveen M N S

2016년 2월 7일

Decent course. Not very satisfied with the assignments as they are suited for graphlab

교육 기관: Carlos A C L

2021년 1월 25일

all lectures are obsoleta, and it's neccesary to install a WSL, the rest very well.

교육 기관: Saket D

2018년 2월 28일

Would have been great if anything compatible with python 3 was used in the course.

교육 기관: kaushik g

2018년 3월 25일

Content was good but was few years old and things are pacing up a bit these days.

교육 기관: amin s

2019년 5월 29일

primitive course, didn't expect this low standard from university of Washington

교육 기관: Rajiv K

2020년 6월 20일

Have to improve for other environment.

have to explain other alternative too.

교육 기관: Vamshi S G

2020년 6월 27일

i think the course should be updated, graphlab and some other are outdated.