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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

13,055 évaluations
3,105 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 3,033 Avis pour Machine Learning Foundations: A Case Study Approach

par Nguyễn T T

13 oct. 2015

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


9 oct. 2020

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

par Nebiyou T

7 juin 2017

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

But they were practical.

par Thomas M G

21 févr. 2018

In my view, too much focus on GraphLab.

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

par Zizhen W

16 oct. 2016

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

par Rajdeep G

7 sept. 2020

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

par Tilo L

20 mai 2022

I​ntresting topics get broadly introduced, sadly the course it outdated at a number of occasions...

par adam h

8 févr. 2016

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

par Reem N

23 juin 2022

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

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 !!