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Retour à Machine Learning Foundations: A Case Study Approach

Avis et commentaires pour d'étudiants pour Machine Learning Foundations: A Case Study Approach par Université de Washington

13,027 évaluations
3,100 avis

À propos du cours

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....

Meilleurs avis


16 oct. 2016

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


18 août 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.

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2876 - 2900 sur 3,024 Avis pour Machine Learning Foundations: A Case Study Approach

par adam h

8 févr. 2016

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

par Cameron B

20 avr. 2016

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

par Uday K

1 mai 2017

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

par Alexander B

4 nov. 2015

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

par Naveen M N S

7 févr. 2016

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

par Carlos A C L

25 janv. 2021

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

par Saket D

28 févr. 2018

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

par kaushik g

25 mars 2018

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

par amin s

29 mai 2019

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

par Rajiv K

20 juin 2020

Have to improve for other environment.

have to explain other alternative too.

par Vamshi S G

27 juin 2020

i think the course should be updated, graphlab and some other are outdated.

par Julien F

16 nov. 2017

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

par Marco M

4 déc. 2015

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

par Alejandro V

13 nov. 2020

TuriCreate is not the apropriate tool for practical Machine Learning

par Pawan K S

15 mai 2016

Nice introductory course but too much dependence on graphLab create

par Jesse W

24 déc. 2016

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

par Tushar k

30 nov. 2015

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

par Konstantinos L

8 janv. 2018

Nice course but too easy. Assignments should be more difficult

par Seong H M

25 sept. 2021

Problems and files and videos not updated base on the changes

par Felipe A S S

23 janv. 2021

The libraries used on the course are a little bit unsopported

par Nadeem B

27 juil. 2021

Concepts and explanation is great but using outdated modules

par Atharv J

14 sept. 2020

The course should be taught in pandas rather than graphlab.

par Max F

10 janv. 2016

Not a bad course, but extremely basic. Was expecting more.

par Adrien L

2 févr. 2017

No good without the missing course and capstone projects

par Aleksey C

11 déc. 2016

....mmm fsdfg gsgsd sgsdgsdg sdsdgsdg ggsgsd sgdsdgsg