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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
13,082 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

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.

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

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2551 - 2575 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par Krishna P S

13 déc. 2018

course is really good with real life examples. Able to correlate well with the concepts

par Jay D S

18 sept. 2020

this course should include some more coding about python in manchine learning and knn

par Ibrahim G

29 août 2017

it's very cool base and i hope next specialization course will get more into details.

par Deleted A

23 oct. 2017

The first week was a little chatty but the content of the rest of the weeks is good.

par Chin-Teng H

15 juil. 2017

bomb bad awful interest present immutable sad great time tack how hungry hungry opps

par Hakim L

3 déc. 2018

Good course despite the technical issues with GraphLab Create in Coursera Notebook.

par Chenkai Z

10 oct. 2016

Good on presenting and using ML tools, but the part of principle is not good enough

par Mateusz B

31 déc. 2015

I enjoyed this course but I think assignments could be a little bit more difficult.

par Satyam R

15 sept. 2020

Thanks a lot for providing such intuitive approach towards the ML and DL Concepts.

par Kim K

23 mars 2016

a very good introduction for machine learning with good examples and explainations

par Alireza A

13 sept. 2021

good course, but the diffrence between turicreate and graphlab is a bit suffering

par Shyam A

7 juil. 2020

good, But check whether your pc can run on graphlab before taking up this course.

par Sachin R G

13 juin 2020

Need some improvement like much more focus on statistical concepts behind program

par Shashikant K

9 juin 2020

This is very good course. This is helpful for me. Some problem on using graphlab.

par Michelle B

1 juil. 2021

The course needs to update the laboratory files, since the commands are outdated

par Anurag G

22 juil. 2020

Preety good course but instead of Sframe , i prefer pandas and sklearn libraries

par Durga P S

9 sept. 2018

Very nice foundation course in Machine Learning especially with GraphLab create.

par Henrik

2 juil. 2016

Very nice content but dont like we use graphlab since i wont use it after course

par Liebesakt S

28 mars 2016

Last module on Deep learning is not explained well as compared to other modules.

par Xun Y

8 sept. 2018

great introductory course to machine learning, includes almost all the aspects.

par Zynab S

30 juin 2016

very good for one who has no idea about machine learning , but I dont like dato

par Bruno K

12 déc. 2015

very nice! A little bit more of reading material would be interesting, though..

par MUBEEN M

29 janv. 2021

hands on material is overly simplified perhaps because it is foundation course

par Ankita S

14 oct. 2020

Great course !! With practical knowledge and the trending topics are captured.

par Mrutyunjaya S Y

16 mai 2020

It given more understanding of all concepts..Its really helpfull for beginners