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
13,055 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

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.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

Filtrer par :

2726 - 2750 sur 3,034 Avis pour Machine Learning Foundations: A Case Study Approach

par Hiếu N Q

28 déc. 2015

Good for ML newbie

par amit d

3 févr. 2020

nice explaination

par Arnab N

5 janv. 2020

Very nice program

par Rahul S

19 déc. 2020

GREAT EXPERIANCE

par SURUTHI T

5 juil. 2020

more informative

par Oscar M

29 mai 2016

Very insightfull

par Tulasi P D

15 juil. 2020

it is so useful

par Rohit K

17 avr. 2020

very intersting

par shane

22 oct. 2015

Very practical.

par Rohit K S

30 sept. 2020

Good Course!!

par Divyashree

14 sept. 2020

A good course

par SHAHID S

16 mai 2022

Nice Content

par ANURAG Y

30 nov. 2021

good teacher

par Rupali G

2 nov. 2017

good content

par André G

14 mai 2016

Good course.

par 廖敏宏

24 sept. 2020

Very useful

par P.BHUVANASHREE

18 sept. 2020

interesting

par HASNA V N

19 juil. 2020

Good course

par Shubham D

3 déc. 2016

nice course

par Le H P

16 août 2019

well done!

par Daniel Ø

18 janv. 2016

very basic

par Muhammad A K

27 nov. 2020

very good

par Sayam N

25 sept. 2020

Excellent

par Aishwarya S

5 juil. 2020

very nice

par Zhen W

5 juil. 2017

Good ~~~~