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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

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


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.


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

Фильтр по:

2876–2900 из 3,034 отзывов о курсе Machine Learning Foundations: A Case Study Approach

автор: Nguyễn T T

13 окт. 2015 г.

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

автор: ADNAN A G

9 окт. 2020 г.

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

автор: Nebiyou T

7 июня 2017 г.

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

But they were practical.

автор: Thomas M G

21 февр. 2018 г.

In my view, too much focus on GraphLab.

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

автор: Zizhen W

16 окт. 2016 г.

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

автор: Rajdeep G

7 сент. 2020 г.

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

автор: Tilo L

20 мая 2022 г.

I​ntresting topics get broadly introduced, sadly the course it outdated at a number of occasions...

автор: adam h

8 февр. 2016 г.

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

автор: Reem N

23 июня 2022 г.

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

автор: Cameron B

20 апр. 2016 г.

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

автор: Uday K

1 мая 2017 г.

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

автор: Alexander B

4 нояб. 2015 г.

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

автор: Naveen M N S

7 февр. 2016 г.

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

автор: Carlos A C L

25 янв. 2021 г.

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

автор: Saket D

28 февр. 2018 г.

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

автор: kaushik g

25 мар. 2018 г.

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

автор: amin s

29 мая 2019 г.

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

автор: Rajiv K

20 июня 2020 г.

Have to improve for other environment.

have to explain other alternative too.

автор: Vamshi S G

27 июня 2020 г.

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

автор: Julien F

16 нояб. 2017 г.

Some quiz questions were vague and/or ambiguous, or not covered in talks.

автор: Marco M

4 дек. 2015 г.

Too much synthetic on very important parts, too much focused on graphlab

автор: Alejandro V

13 нояб. 2020 г.

TuriCreate is not the apropriate tool for practical Machine Learning

автор: Pawan K S

15 мая 2016 г.

Nice introductory course but too much dependence on graphLab create

автор: Jesse W

24 дек. 2016 г.

It is better if allow me upgrade only when I finished this course.

автор: Tushar k

30 нояб. 2015 г.

Good course to begin machine learning with but it's too easy !!