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Вернуться к Machine Learning Foundations: A Case Study Approach

Отзывы учащихся о курсе Machine Learning Foundations: A Case Study Approach от партнера Вашингтонский университет

4.6
звезд
Оценки: 13,058
Рецензии: 3,107

О курсе

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....

Лучшие рецензии

BL

16 окт. 2016 г.

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

PM

18 авг. 2019 г.

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.

Фильтр по:

2826–2850 из 3,035 отзывов о курсе Machine Learning Foundations: A Case Study Approach

автор: Anirudh A

26 февр. 2021 г.

Good with concepts. But would have been better if a standard library like scikit were used rather than SFrames, Turicreate and Graphlab for the sake of easing things out which actually is not very convenient for a lot of students. (atleast during the learning cycle)

автор: David K

1 мар. 2016 г.

I think that the course is redundant, it is to general, trying to capture to much, and using a commercial program tool that's doing to much behind the scene.

The second course in the specialization is really great though and you wont miss anything if you skip ahead

автор: Varun J

24 сент. 2015 г.

A lot of problems with software installations. But, the professors for this class seem to be very passionate about the course and they teach well. If not for a lot of problems faced during software installations(which is still not resolved), would have given 5 stars

автор: Michael C

10 апр. 2016 г.

Really just an overview of the topics to be explained in detail afterwards.

Big plus for the use of python + notebooks but otherwise, if one is interested just in the overview and not in all the specialization, maybe the Andrew NG course is more detailed.

автор: Bernardo G C

8 июня 2016 г.

El curso tiene mucho potencial, pero hay que afinarlo.

Pienso que los vídeos deben ser reeditados. Tienen errores y conceptos confusos. Deberían ser tan claros como para lograr tomar buenos apuntes y usarlos en las tareas. Las tareas son casi mecánicas.

автор: Rishi H

11 июня 2019 г.

Content and material is good and the trainers are good. Only issue i found is course assignments are heavily dependent on Sframes and graphlab which does not work most of the times.,they should go with panda libraries which is easily accessible.

автор: Aman S

14 июня 2018 г.

The worst thing about this course is graphlab. Trying to run it since last 10 days with the help of every available online resources, but in vain. There are many flaws in graphlab. I tried a hundred times to view images in graphlab, but in vain.

автор: Juarez B

12 янв. 2017 г.

This course introduces the key topics of Machine Learning, but the math behind the algorithms is not explained and the programming exercises are too easy. Unfortunately, it also relies heavily on graphlab instead of using open source software.

автор: Mohit S S

7 авг. 2018 г.

Course contet is ok. But, intructors really need to teach in a platform neutral way or some other popular library for which ample support is available. In my opinion, learning a tool which is nowhere used in te industry is not a good idea.

автор: Tarek M s

5 нояб. 2017 г.

the course is good for starter but according to its repetition I waited more .

one star down for many useless information in lectures about Amazon products and so on.

one star down for forcing using unpopular python library .

автор: Piyush K P

24 окт. 2016 г.

thanks to prof and cousera for this wonderful course. I wish the programming part was taught separately from basic. I have taken the previous course which was case study approach with respect to which it was slightly tough.

автор: Jerome B

19 дек. 2017 г.

The teachers are nice and the content is pretty interesting, but they keep talking about the Capstone that we actually won't do. That make me wonder if it's worth continuing, and wonder why they cancelled it eventually.

автор: Gregory T

30 окт. 2016 г.

This was a valuable introductory survey course. For me, the challenge came from my unfamiliarity with Python not the material. I would rate this class as "entry level" for anybody with a college-level technical degree.

автор: Brandon P

10 мар. 2018 г.

There were a lot of assumptions made about my math background. Terms and concepts were used that are foreign to most people and while the forums were helpful it was interesting to see that this is a common feeling.

автор: Mohammad A

22 июля 2019 г.

Course include great knowledge, but when coming to work on tools, they are using old method like we have python 3.7, but course is going through python 2.7 and also older version. That's creating confusion somehow

автор: Ivan P

6 мая 2016 г.

It's not a bad course, but it forces students to use GraphLab, a framework created by one the professors teaching the course, instead of using scikit-learn, a widely used framework for machine learning in Python.

автор: chris s

27 янв. 2016 г.

This course has so much potential but is based on proprietary software. The instructors are excellent and the content is really good. It would get 5 stars if it was based on all open source software.

автор: Nishant K

31 окт. 2020 г.

Great approach with basic explanation of applying and importance of the domain in read world examples. Could have been more in depth in few areas but hopefully will be taken care in following courses.

автор: AHMED E A

23 июля 2020 г.

The course needs to be updated....I have hard times installing turicreate and graphlab on my laptop... at the end, I had to use google collab....

I guess this course needs to use tensorflow instead...

автор: Luis F A C

5 дек. 2020 г.

Aprendí muchas cosas interesantes. Actualmente es grande la dificultad para realizar las prácticas de programación con la librería que usan "graphlab" la cual no se relaciona my bien con windows.

автор: Tom v S

5 июня 2018 г.

In and of itself, the content of the course was pretty good. However, after working through 2 deep dive AI courses of each 6 months, obviously this particular course was not much of a challenge.

автор: Diego A

24 окт. 2016 г.

The Professors and the lectures were excellent. Homeworks are way to easy. Would like to use open source tools like pandas and sci-kit learn instead of proprietary tools like graphlab.

автор: Neelam

18 мая 2020 г.

I cannot download all the software needed specifically Turicreate, despite the provided link it shows never-ending errors, after a week of trying I had to give up the course since.

автор: Kenny J

21 мая 2020 г.

This course needs to be updated. It's hard to follow the notebooks since the lecture was on GraphLab, and some of the explanations were not elaborate enough, especially Week 6.

автор: Zein S

17 янв. 2018 г.

I like more to work with sklearn rather than GraphLab..

Actually many recommended this course to me, and I expect more excitement in the next courses in this specialization