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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


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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2626 - 2650 sur 3,064 Avis pour Machine Learning Foundations: A Case Study Approach

par Nandan S

15 mars 2018

very good overall. The last week (Neural networks) is a little too fast.

par Ramesh S

14 mars 2018

A good and quick introduction to ML. Like the Case Study based approach.

par Anastasiia

2 févr. 2018

OK course if you don't have any background knowledge. Graphlab oriented.

par Aaron M

2 juil. 2017

Seems a bit old but it was a great way to introduce myself to the basics

par Matías G

7 oct. 2016

Great Course, just felt little weak the last module about deep learning.

par Stuart L

18 déc. 2015

a good introduction of the topics. I like the ML diagram in each module.

par Thirumala V S J

10 févr. 2022

Course is kind a old and some dependencies are not working as explained

par Lucia d E P

5 févr. 2018

I enjoyed the course and the fact that it uses Python for the exercises

par Xavier H

8 août 2016

A good introduction tot he tools and possibilities of machine learning.

par Zhe W

27 oct. 2015

Useful course to get general idea to get onboard with Machine Learning.

par Leon

1 oct. 2019

Goes through many topics, but not as in depth as one would have liked.

par Jacques J

8 sept. 2017

Was so good to get some exposure to the different areas of application

par Sandeep K S

5 janv. 2016

Good course with the overview of different machine learning techniques

par fredfoucart

10 déc. 2015

A good global introduction and simply explained. With fun as well....

par Ali N

13 nov. 2015

Really great course content, but the assignments could become better.

par Harshal M

18 août 2017

Great Course!! Helped me learning new things. Great way of teaching.

par federico w

4 avr. 2016

Great course. Super case driven approach, professors are very clear.

par أحمد ج

6 août 2019

wish to use more common ML libraries, but the content was very good

par Kushvanth R

21 janv. 2021

All is well, but instructors could have used more common libraries

par Bruno G E

17 avr. 2016

Just the tip of the iceberg, you'll want to dive in on each topic.

par Tina W

2 avr. 2019

Good Intro course and familiarize yourself with iPython notebook.

par sami j

26 déc. 2017

pretty good - wish there was more info on the internals to models

par Alexander P

17 oct. 2016

Interesting intro class. Will very much leave you wanting more.

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