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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


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


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.

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2651 - 2675 sur 3,065 Avis pour Machine Learning Foundations: A Case Study Approach

par Paul B

21 juil. 2016

Good introduction, the python quick description is short enough.

par Pramod J

17 oct. 2020

Contents are up to mark and very helpful in learning the course

par Kunal B Y

25 juin 2020

it will be better if the videos are also updated to turi create

par Mandar G

31 mai 2020

Both the Instructors were very good at providing the knowledge!

par Anurag U

2 nov. 2016

Its a good course for those who want to learn ML with Graph Lab

par Ahmad B E

5 déc. 2017

Good course for ML except it depends a lot on GraphLab Create.

par James S

7 oct. 2016

dont really like the dependency with dato sframe or prop tools

par Paolo s

5 oct. 2016

It would be perfect if also cover a section on spark an mllib.

par Marco J

14 janv. 2022

Klasse Kurs, nur bei Graphlab vs. Turicreate etwas verwirrend

par yangxiaoqi

29 janv. 2018


par Johan M

9 juin 2016

Excellent course. Looking forward to the rest of the courses.

par David B

4 déc. 2015

A nice introduction to the various machine learning concepts.

par P V P

28 juin 2020

its very basic just used a python module in the whole period


3 juin 2020

i understood many more in this course i understood properly.

par kumar p

15 oct. 2015

Nice for learners who want to jump start in machine learning

par Swapnil A

6 sept. 2020

Would have been a 5 start course if the content was updated

par R C

9 oct. 2020

A bit superficial, better to merge with following courses

par C M R

4 août 2020


par Gopisetty k p

24 juin 2016

This is very good material for machine learning starters.

par VJ

16 nov. 2015

good material. good presentation skills of the instructor

par Le D N

11 juil. 2021

Everything is good except the library used in Course ;(

par João G B A V

4 déc. 2017

Muito bom, os exemplos onlines poderiam ser interativos.

par Milan C

20 oct. 2017

Nice overview about different Machine Learning concepts.

par Jjclof

17 oct. 2016


Good teachers.

But a bit too simple. 4/5


par Markus M

10 févr. 2016

Good structure, but maybe a bit too basic and slow pace.