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

4.6
stars
12,429 ratings
2,974 reviews

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

PM
Aug 18, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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

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2826 - 2850 of 2,887 Reviews for Machine Learning Foundations: A Case Study Approach

By Matthew F

Jul 21, 2019

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

By Joseph J F

Aug 20, 2017

It is more a course in using the tools designed by the teachers than machine learning. It might do something for a less experienced user in programming, but I didn't find it much use. The overview of Machine Learning tasks isn't bad.

By Andras H

May 31, 2020

on one hand good... on other hand annoying ( mixing graphlab and turicreate... shitty wording of the assignment task, info added as side note which was vital for the assignments...etc.) The curse material would need a refresh.

By Sunil T

May 24, 2020

SFrame data do not support by an updated version of the Python, so student won't able to finish their assignments. So instructor need to update the materials and database which is supported by a new version of Python

By Tudor S

Apr 22, 2018

The Assignments and Quiz questions are hard to read and comprehend.

Although individually the course presentations are ok, overall this course isn't a very relevant or coherent introduction to Machine Learning.

By Taylor I

May 11, 2020

Feel like I have been duped in a way. No capstone project and you are pretty much forced to use Turi Create (proprietary/black-box version of pandas), which I found incredibly hard to install and use.

By Ashley

Jun 23, 2019

Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.

By Arman A

Feb 16, 2016

The course uses proprietary tools for machine learning and data manipulation, making it effectively useless! However, the material on describing the machine learning algorithms were excellent!

By Annemarie S

May 24, 2019

The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.

By charan S

Jul 16, 2017

If someone is looking for ML foundations and what is ML, they can choose this course. This is very basic course and i feel should be excluded from the ML specialization.

By Eiaki M

Mar 5, 2016

One would learn a thing or two, but the course is very sparse compared to other machine learning courses, and I didn't feel that it was worth the time and the cost.

By Robert M

Oct 27, 2015

I do not like this course being tied to a commercial product. In my opinion it should be using an open source python library and not focusing on the Dato product.

By Evlampi H

Nov 5, 2015

The framework is ok, but it would be more insight on the functions would be much more amplifying the learning process.

Good working examples, though!

By shanky s

Apr 26, 2021

I thought that indepth will be taught and enrolled for this course, but unfortunately its only basics. I wasted my enrollement

By Piotr T

Oct 6, 2015

it's rather a course on using API of proprietary software with very very basic background on the actual math underneath

By David F

Dec 2, 2015

I didn't like the python environment, I thought it will be more like Ng's course. Nice explanations, but for amateurs.

By Patryk H

Oct 14, 2015

Due to many technical issues with GraphLab lib I have to reduce acitivity in this curse for only video viewing :(.

By Elgardo E

May 29, 2020

Course videos are outdated and requires time to investigate and research. This causes wasted time and effort.

By David H

Oct 31, 2015

Very, very high altitude introduction presented in a seemingly confused way with a lot of product placement.

By Zuozhi W

Feb 7, 2017

TBH this class's experience is not good. The lecturers seem unprepared and they talk very repetitively.

By Suhasini L

Sep 4, 2020

Not given details like what is a vector? people from non technical backgrounds will have tough time

By ashish s g

Feb 15, 2017

Very good course material. However, Graphlab is no longer free to use for commercial purpose.

By Mark F

Dec 19, 2015

This course is to much about graphlab and not enough about the mechanics of machine learning.

By Najmeh R

Oct 4, 2016

The subjectes are not learnt deeply and precisely. Too summarized and vague!

By tiafvoonug k x

Jan 6, 2016

As a non programer, or mathematician, this course is too hard to follow.