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

4.6
stars
13,542 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

AH

Mar 27, 2022

very nice course.If you have basic knowledge of python datastructure then this course is best to start data science.All contents are beginner friendly which makes this course easily understandable.

MK

Jul 20, 2019

A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!

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3026 - 3050 of 3,159 Reviews for Machine Learning Foundations: A Case Study Approach

By Christos M

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Feb 1, 2023

The assignments were really short and extremely easy

By HITESH D

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Jun 15, 2020

Installing software parts gave me a very hard time.

By Bastian M P

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Jun 1, 2016

Could go a little more in detail on the algorithms.

By Jaime O

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Jan 31, 2017

The Deep Learning part needs to be improved

By Chen S

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Oct 26, 2015

Very basic, the quizzes aren't clear enough

By Li-Pu C

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Oct 29, 2020

A little bit too easy, but good for rookie

By Harsh V K

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May 8, 2019

Should use Python 3 instead of Python 2

By Deleted A

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Apr 3, 2021

sofware guideline is quiet useless

By Yu G

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Feb 7, 2021

No idea what to write here...

By Jorge C

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May 29, 2016

It is a very simple course.

By Aditya A

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Apr 10, 2025

few quiz answers are wrong

By Ricardo S

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Aug 10, 2021

Feels a bit out dated

By RAGHUPATHI R R

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Jun 25, 2020

Good for knowledge

By Fredick A S

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Apr 6, 2018

No python..

By Nasimul J F

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Aug 16, 2020

THANK YOU.

By Kai C

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Nov 24, 2015

Too easy

By Geetha G

•

Aug 15, 2021

good

By Anshu R

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Sep 12, 2020

good

By 18103048 H - S C

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Sep 4, 2020

Good

By MD. S K S

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Aug 22, 2020

cool

By tarun v s n

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Jul 23, 2020

Good

By Abhinav S

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May 10, 2020

good

By Bindra B

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Jun 20, 2021

k

By CHEE W M

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Sep 26, 2019

V

By Andrew S

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Dec 3, 2016

The content of this course is interesting, I liked the examples, and the material gave an interesting overview of different aspects of machine learning. From that perspective, the course is as advertised. But, where this course goes wrong is value for money - it is very superficial and not worth what is charged.

As noted by others, this is not a course for learning so much as an advertisement for the instructor's own pay software and their other Coursera courses. I'm not against that per say if it was entirely free, but charging for an advertisement is ridiculous. In my case I thankfully started with the free model so I didn't lose out, but I could see others being dissapointed. I strongly recommend starting the material with a free signup and only pay if you really want the extra grading.

My other main problem was with the pace and detail in the course. I would have liked more detail, but I recognize this was intended to be a high level view so I'll live with that level of detail. The material covered, however, does not need 6 weeks worth of lectures. This course could be ~1/2 as long, cover the same material, and be a MUCH better course.

Other small problems include some poorly edit videos (there are a lot of examples of simple stumbling in the videos that should have meant they do another take), very short videos (maybe a person preference, but the number of <2 minute videos here is annoying, especially when there's a 5-second standard video at the start and end of all videos). All in all, there's just a lot of wasted time.

When signing up for this course I was really excited for the entire specialization - now, not so much. I'll probably try the second course in the series (for free to start) to see if things improve, but ironically this advertisement video has if anything turned me off their other products.