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

13,101 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


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


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 :

76 - 100 sur 3,049 Avis pour Machine Learning Foundations: A Case Study Approach

par Peter F

30 mars 2020

par Rithik S

26 mai 2020

par Yakubu A

23 déc. 2020

par ye

31 janv. 2021

par Jitendra S

29 avr. 2016

par Ashutosh N

30 mai 2020

par Krupesh A

15 févr. 2019

par Shreyash N S

20 mai 2020

par Japman S

6 juin 2020

par YM C

6 sept. 2019

par Darren R

13 oct. 2015

par Kaushik M

1 mai 2016

par D. F

2 févr. 2021

par Rohit

19 avr. 2020

par Shibhikkiran D

13 avr. 2019

par Diogo P

15 févr. 2016

par Karthik M

27 déc. 2018

par Alexandru B

21 janv. 2016

par Mallikarjuna R V

17 janv. 2019

par Sundar R

19 août 2020

par akashkr1498

18 janv. 2019

par Yuvraj S

1 févr. 2019

par Jaime R

17 déc. 2018

par Ezequiel P

7 nov. 2020

par Ayush G

5 juin 2020