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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

13,059 évaluations
3,108 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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3001 - 3025 sur 3,035 Avis pour Machine Learning Foundations: A Case Study Approach

par Osamudiamen I

15 avr. 2022

Teaching with an unfamiliar tool that is so difficult in installing i.e Sframe, Graphlab and turic create. You should first start with the process of installation before starting your teaching. Wasted resources.

par Santosh K

14 nov. 2021

Uses Outdated graphlab. Very difficult to follow the instructions. Tried using the turicreate but its very problematic. The courses should ease of use tools rather than something outdated.

par Kyle D

9 juil. 2020

The software was so difficult to install. It did not seem worth it to learn a completely new way. Unfortunately, I dropped it because the case study approach looked interesting.

par Ruth P

20 août 2021

Couldn't even get started. Very disappointed that the links to the software required are so out of date and there is no help. Want to un-enroll but can't do that either.

par Sahil K

24 nov. 2020

Software installation was a big trouble and took nearly month as this course was in graphlab, but we need to use turicreate or other toolset.

par Harry W

6 janv. 2022

You'll spend more time trying to get the correct versions and plugins working than you will studying. The course needs updating.

par Konstantinos V

3 nov. 2021

Started this course with great enthusiasm but end up with frustration on being unable to install Turi Create with python 3.10.

par Bharath K V

13 déc. 2021

The course is very very confusing. Old material without updating to the latest format. No clear instructions. Half baked.

par Aaron B

29 juin 2022

This course is out of date and really useless at this point. Do not waste your time. None of the code or examples work.

par Muhammad A A J

12 juil. 2021

This course is very old and waste of time because libraries used in here are not available for new versions of python.

par Kunal V

22 sept. 2020

its very very hard to setup jupyter notebbok and installing turicreate ,also takes a lot of efffort in practical quiz

par Marius M

8 juil. 2020

Unable to install turicreate. No troubleshooting instructions, only a link to a blog post that offers little help.

par Florea G A

11 oct. 2020

Turicreate installation is a hot stone. for that reason I'm not going to pursue this specialization.

par Bowen S

25 nov. 2021

poorly structed with questions & answers

the packages used for the course has been out dated

par Anoop B

2 déc. 2020

Terrible course. Poor presentation, unnecessary talks, clumsy video and outdated content.

par andrew r

25 nov. 2020

A waste of time trying to setup an obsolete environment that is no longer supported.

par Kailash H S N

25 août 2021

very bad , theyuse SF frames which is not in use now ..very hard to do the quiz

par Sudheesh R S

11 juil. 2020

No proper directions as to how to work on libraries to be installed.

par Arpit S

22 mai 2020

Improve the quality of quizes. Need to focus more on algorithm part.

par Pratick B

8 août 2021

I​nstallation of Sforce and turi was not shown adequately enough.

par Mohamed M

28 sept. 2021

import turicreate is hard to install and class based on it

par Eunyoung C

29 août 2020

This course could be better to use general python library.

par Christian C

5 juin 2021

El curso es bueno pero esta completamente desactualizado

par Sunita b l

4 juil. 2020

Provide the good notes and video so all concept clear.

par Melissa F

2 août 2021

cannot get the tools installed to do any of the work.