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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

13,082 évaluations
3,116 avis

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.

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2501 - 2525 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par Christophe M

8 nov. 2015

Very didatic approach to machine learning. Easy access and still powerful technique to understand how it works.

par Bhaargavi A

8 mai 2018

Good Course. Teachers have taught it well and the jupyter notebooks are good and give a good deal of practise.

par Soham K

28 mai 2020

Overall it has been a great experience. But in my opinion, the course videos should be updated to TuriCreate.

par Kuldeep K

11 mai 2020

Kindly change the GraphLab package system, the majority of the compiler doesn't support this.

Else it was good

par Azhar B T

16 sept. 2019

the course is good but rely on graphlab and lack of hands on with python is the reason i cannot give 5 stars.

par Philip L

12 nov. 2016

Some quiz questions' answers are incorrect and instructors need to update the quit to reflect correct answers

par Islam M

19 avr. 2016

-great introduction .

-go through a lot of exciting topics.

-but the implementation part is boring something.

par M.sakif m

16 nov. 2015

Interesting class, but should have used open source python libraries instead of restricted license libraries.

par mohd s

15 sept. 2019

Amazing they guide me help .Special sir working on project .It clear my concept with the real world example.

par Vasudev V

19 févr. 2017

It would be great if you could intersperse theory and practical sessions. Otherwise, a very useful course...

par Mohit P

12 mars 2019

This course is a great starting point who has no earlier experience of ML. . Cheers to the course makers!!!

par Yuting S

26 févr. 2019

Wonderful course.

The only problem is that I can't review the course materials after completing the course.

par C K S

28 mai 2018

Course was nice and especially special thanks to both the faculty's who make us to understand the course.

par Benson C

4 sept. 2017

Interesting, I never used graphlab before. It would be better if this course went through algorithm deeper.

par Javier M

17 juil. 2017

Great introduction to the topic. I think the juniper notebook is still buggy. Its stability can be improved

par Sergio A M

14 mai 2016

It is a great introduction but this could be done by adding a week more in each of the following courses.

par Veera R

11 mars 2016

Case study approach followed in this courser is very use full and helps to understand the methods better.

par Clotilde D

5 janv. 2016

good overview, a little bit hard regarding the deep learning course, that would require more explanations

par Hitesh D

1 oct. 2019

The course has helped me understand basic of Machine learning and has created interest for me.

Thanks :)

par Pushpak T

13 mars 2016

Gives a high-level understanding of machine learning concepts and focuses a lot on its application part.

par Rajkumar D

2 mars 2016

Good Start up with case study approach to just understand what we gonna learn in further specialization.

par ashish

2 mai 2018

The course from coursera was well delivered. Albeit, their seems to be too much dependency on graphlab.

par Mohit A

12 nov. 2019

Great learning experience! We should have option of videos with exercises using Pandas & scikit learn.

par Michele P

5 sept. 2017

Good introduction, although the implementation exercises use mainly GraphLab which is not open-source

par Santosh K W

16 févr. 2016

Useful course. Covered good amount of usecases for Machine learning concepts with handson experience.