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

4.6
étoiles
13,206 évaluations

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

SZ

19 déc. 2016

Great course!

Emily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

BL

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

Filtrer par :

2726 - 2750 sur 3,065 Avis pour Machine Learning Foundations: A Case Study Approach

par Priyanka J

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18 juin 2020

Good work but need more explanation

par Abhinav M

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1 avr. 2018

Great material to try out ourselves

par Dominik S

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26 juin 2017

Very nice. I think I learned a lot.

par Sunny D

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3 mai 2020

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par Shishir S

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28 mai 2017

Good course for the beginners ....

par kulbhushan

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27 oct. 2016

Very informative and useful course

par BIAN D

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15 juin 2016

homework could be more complicated

par A d v

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9 janv. 2022

i loved it excellent explanation

par Royal P

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18 nov. 2020

very knowlegdeful and interesting

par Reginald A L

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20 sept. 2020

A good course to learn. Thank You

par Li Y

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15 oct. 2017

look this approach for beginners!

par Yunqi H

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24 janv. 2020

Great intro class but very fun !

par Nitin K

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4 mai 2017

Pretty good introductory course.

par Arish A

•

8 juin 2016

A good introductory level course

par Mario A G J

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26 avr. 2016

Good for an introduction class.

par Weiyi W

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11 juin 2018

Quizs are harder than lessons.

par Mehul P

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1 août 2017

Nicely explain use case of ML.

par Jijo T

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6 oct. 2015

I love the hands on exercises.

par Mazen A

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9 oct. 2016

the best introduction for ML.

par Rishabh C

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23 juil. 2020

Awesome course to start with

par Rakesh G

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15 avr. 2019

A good beginners guide to ML

par RISHAB P H

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15 avr. 2020

add more practical's please

par Mahesh B

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10 oct. 2019

Good start for ML beginners

par Poornima S

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18 févr. 2019

It is designed really good.

par Hyeong R J

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2 févr. 2017

Good lecture and practices.

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