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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: 13,054
Рецензии: 3,105

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

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.

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

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2951–2975 из 3,032 отзывов о курсе Machine Learning Foundations: A Case Study Approach

автор: Matthew F

21 июля 2019 г.

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

автор: Joseph J F

20 авг. 2017 г.

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.

автор: Andras H

31 мая 2020 г.

on one hand good... on other hand annoying ( mixing graphlab and turicreate... shitty wording of the assignment task, info added as side note which was vital for the assignments...etc.) The curse material would need a refresh.

автор: Sunil T

24 мая 2020 г.

SFrame data do not support by an updated version of the Python, so student won't able to finish their assignments. So instructor need to update the materials and database which is supported by a new version of Python

автор: Tudor S

22 апр. 2018 г.

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.

автор: Taylor I

11 мая 2020 г.

Feel like I have been duped in a way. No capstone project and you are pretty much forced to use Turi Create (proprietary/black-box version of pandas), which I found incredibly hard to install and use.

автор: Ashley

23 июня 2019 г.

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.

автор: Arman A

16 февр. 2016 г.

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!

автор: Annemarie S

24 мая 2019 г.

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.

автор: Charan S

16 июля 2017 г.

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.

автор: Eiaki M

5 мар. 2016 г.

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.

автор: Robert P M

27 окт. 2015 г.

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.

автор: Kishore Y

25 сент. 2021 г.

This is a good idea to use the case study approach. However, there are issues with files and program setup that stopped me from continuing with the course.

автор: Evlampi H

5 нояб. 2015 г.

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!

автор: shanky s

26 апр. 2021 г.

I thought that indepth will be taught and enrolled for this course, but unfortunately its only basics. I wasted my enrollement

автор: Simone V

21 апр. 2022 г.

It started nice but there are some basic aspects, like installing Turi Create, neglected. I had to withdraw from the course.

автор: Piotr T

6 окт. 2015 г.

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

автор: David F

2 дек. 2015 г.

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

автор: Patryk H

14 окт. 2015 г.

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

автор: Elgardo E

29 мая 2020 г.

Course videos are outdated and requires time to investigate and research. This causes wasted time and effort.

автор: David H

31 окт. 2015 г.

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

автор: Zuozhi W

7 февр. 2017 г.

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

автор: Suhasini L

4 сент. 2020 г.

Not given details like what is a vector? people from non technical backgrounds will have tough time

автор: ashish s g

15 февр. 2017 г.

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

автор: Mark F

19 дек. 2015 г.

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