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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

13,204 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


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.


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

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2751 - 2775 sur 3,063 Avis pour Machine Learning Foundations: A Case Study Approach


30 juin 2018

Nice course for beginners

par Vinicius G d O

23 juin 2016

Good introductory course.

par José T G R

1 nov. 2015

Very good!!! Excellent!!!

par Tushar A

13 juil. 2020

This is a nice course..

par Fernando S

20 août 2017

Easy going, very good!!

par Godwin

4 juin 2017

Very interesting :) WOW

par Annie I R

4 janv. 2016

This is a great course.

par Mayur S

18 janv. 2017

its good, if new to ML

par Shikhar S

8 déc. 2020

Great course to start

par Wridheeman B

30 juin 2020

It was a great course

par Eric S

5 janv. 2016

Pretty good, overall.

par Mahajan P J

26 déc. 2019

The course was good.

par Richik G

11 juil. 2019

computer vision best

par Pieterjan C

2 oct. 2017

very useful to start

par Shreeti S

16 août 2017

Good to start with.

par Waquar R

8 août 2016

this is really good

par Vivek A

18 avr. 2016

Enjoyed this class.

par Fei F

22 déc. 2015

Easy for beginners.


30 août 2021

it helped me a lot

par Explore I

15 nov. 2019

Awesome Experience

par Binil K

10 janv. 2016

Really great one!!

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