Chevron Left
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

4.6
étoiles
13,102 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.

Filtrer par :

51 - 75 sur 3,049 Avis pour Machine Learning Foundations: A Case Study Approach

par Arun J

18 sept. 2018

par Mike L

6 sept. 2016

par Jakub A

16 mars 2020

par Hamid N

26 déc. 2020

par Sujith S M

7 juin 2020

par SIVA S

31 mai 2020

par Winston H

24 févr. 2020

par Toma K

11 juin 2019

par Pablo S

22 juil. 2019

par Xing W

3 juil. 2016

par Eduardo R R

23 sept. 2015

par Dmitri K

26 mai 2016

par sravan

13 oct. 2016

par Mario L

24 nov. 2015

par Lester L

21 mars 2020

par Tim B

4 juin 2019

par Phillip B

25 sept. 2015

par Chandrakant M

6 sept. 2016

par Nitin K

12 sept. 2019

par Nik M N N G

11 févr. 2020

par Tomas R L

1 oct. 2021

par Nafi A K

15 oct. 2017

par Hector G A ( O - T ( A

22 janv. 2022

par Vishok

12 oct. 2020

par Mihir I

13 mai 2020