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

4.6
stars
13,485 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

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.

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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2976 - 3000 of 3,140 Reviews for Machine Learning Foundations: A Case Study Approach

By Tùng N

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Oct 13, 2015

the lectures are pretty great, engaging. the assignments stick with the lab exercise. the forum pretty active.

By ADNAN A G

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Oct 9, 2020

old and bad quality but very good explanation half of the course is programming there is no machine learning.

By Nebiyou T

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Jun 7, 2017

Some of the modules lacked polish and have not been updated since initial recording!

But they were practical.

By Thomas M G

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Feb 21, 2018

In my view, too much focus on GraphLab.

This is a problem because GraphLab doesn't seem to be open source.

By Zizhen W

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Oct 16, 2016

Some instructions of the programming assignments are not all that clear, which wasted me a lot of time.

By Rajdeep G

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Sep 7, 2020

They should upgrade the course in respect to python 3. Irrespective of that the theory part was great

By Tilo L

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May 20, 2022

Intresting topics get broadly introduced, sadly the course it outdated at a number of occasions...

By adam h

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Feb 8, 2016

would vastly prefer if this was taught using sckit-learn and pandas, given their broader use.

By Reem N

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Jun 23, 2022

It is very general however it gave me an insight to different machine learning applications.

By Cameron B

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Apr 20, 2016

The course is ok, the instruction was very poor for the deep learning section of the course.

By Uday K

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May 1, 2017

The theories for the models should be explained in more detail and with few more examples.

By Alexander B

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Nov 4, 2015

lectures were well done, but the strong focus on using graphlab ruined this course for me

By Mehrave S

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Aug 8, 2024

turicreate made lots of problem for me, it was better to instruct it by another package

By Naveen M N S

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Feb 7, 2016

Decent course. Not very satisfied with the assignments as they are suited for graphlab

By Carlos A C L

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Jan 25, 2021

all lectures are obsoleta, and it's neccesary to install a WSL, the rest very well.

By Saket D

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Feb 28, 2018

Would have been great if anything compatible with python 3 was used in the course.

By kaushik g

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Mar 25, 2018

Content was good but was few years old and things are pacing up a bit these days.

By amin s

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May 29, 2019

primitive course, didn't expect this low standard from university of Washington

By Rajiv K

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Jun 20, 2020

Have to improve for other environment.

have to explain other alternative too.

By Vamshi S G

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Jun 27, 2020

i think the course should be updated, graphlab and some other are outdated.

By Julien F

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Nov 16, 2017

Some quiz questions were vague and/or ambiguous, or not covered in talks.

By Marco M

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Dec 4, 2015

Too much synthetic on very important parts, too much focused on graphlab

By Alejandro V

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Nov 13, 2020

TuriCreate is not the apropriate tool for practical Machine Learning

By Pawan K S

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May 15, 2016

Nice introductory course but too much dependence on graphLab create

By Jesse W

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Dec 24, 2016

It is better if allow me upgrade only when I finished this course.