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

4.6
stars
13,492 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

PM

Aug 18, 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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2851 - 2875 of 3,143 Reviews for Machine Learning Foundations: A Case Study Approach

By Daniel Ø

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Jan 18, 2016

very basic

By Muhammad A K

•

Nov 27, 2020

very good

By Sayam N

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Sep 25, 2020

Excellent

By Aishwarya S

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Jul 5, 2020

very nice

By Zhen W

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Jul 5, 2017

Good ~~~~

By Kevin C N

•

Dec 10, 2016

Thanks!!!

By Oriol P

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Mar 30, 2016

Was nice!

By Sreemannarayana B

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Feb 23, 2016

Excellent

By Oumar D

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Feb 21, 2016

Efficient

By DEBASISH M

•

Sep 21, 2020

Like it.

By John M

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Jul 4, 2018

Liked it

By Evan Y

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Dec 23, 2018

So good

By पं.अभिषेक प

•

Jul 25, 2023

GOOD

By Venna S V

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Jan 20, 2022

GOOD

By VYSHNAVI P

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Dec 13, 2021

Good

By SHISHANTH R

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Sep 6, 2021

good

By Deleted A

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Aug 14, 2020

good

By YEDURADA J K

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Aug 10, 2020

nice

By Rohan B R

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Jun 24, 2020

nice

By vishwak

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Jun 21, 2020

cool

By Dr. A S M M R

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Jun 6, 2020

Good

By 楊傑綸

•

Dec 29, 2015

Cool

By 王博

•

Nov 13, 2015

nice

By Brijmohan S

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Mar 22, 2018

V

By Sofia P

•

Mar 12, 2016

I did not have a lot of experience in machine learning, so this course was very good in the aspect of introducing people to machine learning concepts. Most of the times the material was very well explained, and I like the concept of the tutor writing on the screen at the same time they are presenting, personally it helps me more. Some of the quizzes were easy so you did not need a lot of preparation, some of them were more difficult or troublesome, like the quiz for Deep Learning. I also liked the graphlab module, I think that learning how to use it will help me with my own work.

However, as this course does not really go in depth in the algorithms themselves, I feel that after one month and a half I have a basic idea, but I haven't learned much about how to implement machine learning on my own even in basic things, while other courses have more or less the same time frame and are more dense in their material. In my opinion, this whole introductory course would just be just splitted and each of these intrductory weeks would be appended as the first week of the subsequent modules to come. Because anyways, after 4 months in the specialization, if somebody continues to the recommender systems module for example, he/she would have forgotten the basics of this so they would need to cover again the recommender systems week in this course. And from the other hand, if some introduction is again repeated in the subsequent modules, then why have this introductory course anyways?

Thanks.