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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
13,086 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

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

PM

18 août 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

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2651 - 2675 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par Fenjin W

15 avr. 2016

Great course! Hope the slides gets better annotated.

par Prashant D

16 juin 2020

The course was awesome and so does the Instructors!

par Yagyansh S K

2 déc. 2016

Awesome Teaching Technique Used! Kudos To The Team!

par Mitali C

10 juin 2020

It was Amazing course ,with amazing instructors :)

par Avinash P M

13 déc. 2016

Assignments could have been little more difficult.

par Alvin B K

17 août 2020

Carlos and Emily are really cool. They're cool 😀

par 吴青

6 déc. 2017

didn't reach my expectation but still quite good.

par Albert Z

6 févr. 2016

Hands on should have been more involved/dificult.

par Omar H F A

3 juil. 2020

Very Good introductory course to the field of ML

par Gopinath T

14 mai 2019

Well structured course with detailed explanation

par Sam P

16 janv. 2018

A bit light on details but a great first course!

par Qishen S

25 mai 2017

A good overview of ML and tutorial for graphlab.

par Harsh R

18 mai 2020

The course is little bit outdated.

please update

par shadab h

18 nov. 2018

A must do course to start with Machine Learning

par Deleted A

21 janv. 2019

great class to learn machine learning hands on

par Spike Y

30 nov. 2017

I like the explaining about ipython notebook !

par Abid A S

3 août 2017

Good one to start learning some basic concepts

par Amilkar A H M

10 mai 2019

It's a very good introduction to the subject.

par Roberto E

28 nov. 2016

Interesting but a bit weak for my purposes...

par Matic D B

9 févr. 2016

Great course. Maybe a little bit too basic.

par Jayant S

22 oct. 2019

Explanations must be more comprehensible.

par Flavio B

9 févr. 2016

Most of it I already knew, but was nice.

par GOLLAPUDI R C

23 nov. 2015

A good introduction to machine learning

par Charupriya S

21 juil. 2020

It's really nice course for beginners.

par Sankara S K

22 sept. 2018

This is a good 101 course to start ML.