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Avis et commentaires pour d'étudiants pour Machine Learning Foundations: A Case Study Approach par Université de Washington

13,086 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


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.


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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2526 - 2550 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par Cristian C H M

20 juin 2020

The videos are out dated and Turicreate doesn't work on Python 3.8 and the latest version of Ubuntu.

par Jeff L

29 nov. 2015

This was a fantastic course for someone both unfamiliar with machine learning algorithms and python.

par Tomáš Z

30 nov. 2018

Nice intro but it could have been more in depth, not just as a simple graphlab/turicreate tutorials

par Jonathan E

15 août 2017

I liked the interactive python programming however the course could be more rigorous for my tastes.

par Sergey G

23 mars 2019

It'd be great to get rid of showing how teacher type code or write something, it's kind of boring.

par DeepLyrics

11 déc. 2016

Great introductory course for beginners to machine learning and I loved the programming tutorials.

par Alfonso M

26 mars 2016

Instructors are clear and passional about their teaching. I wish the course was slightly deeper.

par E. M S

17 mai 2017

Good overview and programming warm-up. Just need to change the links to from


17 mars 2021

Nice Course and developed a nice understanding from the questions present in the assignments.

par Tomas O

8 nov. 2019

It has a lot of problems with the programs you should install. But the content it's amazing.


14 nov. 2017

This course was a very good introduction to some of the techniques used in Machine Learning.

par Stefan S

4 janv. 2016

Good intro to topics in machine learning as well as to Graphlab Create and iPython Notebook.

par Stanislav B

18 avr. 2020

There were unnecessary problems with data. Last course (Clustering and retrieval is better)

par weimin l

27 oct. 2016

A very interesting course! I learned a lot. Will continue on the next course once it ready.

par Rishabh P

26 juil. 2020

It would be great if you start deployement of things in python also not only in turicreate

par Brent R

29 janv. 2017

Good intro to ML, but would've enjoyed less of the "Black Box" approach in using Graphlab.

par Aaron W

1 janv. 2016

Great introduction to ML concepts! I'm hoping to dig a little deeper in the next courses!

par Patrik L

17 juil. 2017

Would be very useful to have the option to see all the coding examples done with sklearn.

par Tung N

7 juin 2016

Very good balance of concept and practice. Would be 5 stars if all tools are open source.

par KAI S E

3 juin 2016

A Brief understanding achieved! I love the instructor and the way they conduct the class.

par Thomas S

29 mai 2016

Nicely structured and overall a great course. Sometimes a little slow, for my preference.

par Suneet T

7 févr. 2016

Very good course for getting a high level understanding of the Machine learning concepts.

par Franck B

19 juin 2020

Very interesting, didactic but a little bit too based on a specific non-standard library

par Shankarganesh G

11 sept. 2019

It' s really worth spending time to learn this course. It's very informative and useful.

par Piyush M

21 mars 2017

Very good introductory course with an excellent mix of theoretical and hands-on content.