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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

13,206 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


19 déc. 2016

Great course!

Emily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.


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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2601 - 2625 sur 3,065 Avis pour Machine Learning Foundations: A Case Study Approach

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..


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

par mikhil i

1 déc. 2016

The deep learning part of the course needs to be better done. The rest is good

par Ricky W

10 févr. 2016

Very nice introduction to Machine Learning and to Python programming language

par Max D

23 août 2021

id like to see more examples and use others packages different to turicreate

par Daniel B S d S

2 nov. 2016

The course is great, but it would be greater if used open source free tools.

par Igor S

13 avr. 2021

I would improve questions in the quiz, sometimes they are really confusing.

par Bilal S

17 oct. 2016

It' a fine beginner's course. I liked the hands-on approach using SFrames.

par Marco P

4 déc. 2015

The homework assignments were not really about having understood the course

par Sourabh K

30 juin 2020

numpy and pandas are more preferable, but the overall experience was good.

par George B

17 mai 2018

Pretty great course. Really enjoyed it and looking forward to new courses