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Learner Reviews & Feedback for Machine Learning Foundations: A Case Study Approach by University of Washington

4.6
stars
13,527 ratings

About the Course

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....

Top reviews

BL

Oct 16, 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

SZ

Dec 19, 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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2676 - 2700 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Durga P S

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Sep 8, 2018

Very nice foundation course in Machine Learning especially with GraphLab create.

By Henrik

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Jul 2, 2016

Very nice content but dont like we use graphlab since i wont use it after course

By Liebesakt S

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Mar 28, 2016

Last module on Deep learning is not explained well as compared to other modules.

By Xun Y

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Sep 8, 2018

great introductory course to machine learning, includes almost all the aspects.

By Zynab S

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Jun 29, 2016

very good for one who has no idea about machine learning , but I dont like dato

By Bruno K

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Dec 12, 2015

very nice! A little bit more of reading material would be interesting, though..

By MUBEEN M

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Jan 29, 2021

hands on material is overly simplified perhaps because it is foundation course

By Ankita S

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Oct 14, 2020

Great course !! With practical knowledge and the trending topics are captured.

By Mrutyunjaya S Y

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May 16, 2020

It given more understanding of all concepts..Its really helpfull for beginners

By mikhil i

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Nov 30, 2016

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

By Ricky W

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Feb 10, 2016

Very nice introduction to Machine Learning and to Python programming language

By Max D

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Aug 23, 2021

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

By Daniel B S d S

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Nov 2, 2016

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

By Igor S

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Apr 13, 2021

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

By Bilal S

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Oct 17, 2016

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

By Marco P

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Dec 4, 2015

The homework assignments were not really about having understood the course

By Sourabh K

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Jun 30, 2020

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

By George B

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May 17, 2018

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

By Jeffrey v S

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Oct 31, 2017

Content is good but the delivery is somewhat awkward and chatty at times.

By Brennan W

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Feb 4, 2017

Was a good intro to different kinds of ML. Wish we had used SciKit-Learn.

By Nandan S

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Mar 15, 2018

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

By Ramesh S

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Mar 14, 2018

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

By Anastasiia B

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Feb 2, 2018

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

By Aaron M

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Jul 2, 2017

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

By Matías G

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Oct 7, 2016

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