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

4.6
stars
13,545 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

RM

Feb 3, 2022

I was very disappointed with the exclusion of the final courses and the capstone project. The most interesting part of specialization no longer exists and no plausible justification has been given.

RH

Jun 9, 2017

I felt this course did a good job introducing the student to Machine Learning. The examples and hands on assignments brought the concepts home. I was able to use the knowledge immediately at work.

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1776 - 1800 of 3,160 Reviews for Machine Learning Foundations: A Case Study Approach

By Chao P

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Oct 11, 2016

impressive course!!!!!

By Arif A

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May 18, 2016

Very practical course.

By Daniele R

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Dec 15, 2015

molto bravi!

very good!

By ANKIT G

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May 14, 2023

It was really nice ..

By Anuj G

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Feb 6, 2023

A great course for ML

By rebwar k

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Oct 9, 2020

it was amazing for me

By Thientvse

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Jul 15, 2020

Very good, and detail

By Amit L D

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Jun 22, 2020

It was a nice course.

By Annanya J

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Jun 15, 2020

This course is fun :)

By Akash G

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Mar 9, 2019

START basic like star

By Tunuguntla S

•

Mar 28, 2018

very nice interaction

By JOSE R

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Nov 19, 2017

Awesome work. Thanks.

By YangjiHYun

•

Nov 14, 2017

Very GOOD!! Thank you

By Vinoth H

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Jun 16, 2017

Very simple to learn.

By Vijay K

•

Apr 27, 2017

A wonderful course!!!

By Mauro L

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Jan 19, 2017

Excellent Professors.

By Brahmeswara Y

•

Mar 28, 2016

Good overview course.

By Kumar N

•

Feb 15, 2016

Awesome for beginners

By Amine L

•

Oct 26, 2015

Great Course, thanks!

By Robin P

•

Jun 5, 2021

very pleasant course

By KIRAN K

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Aug 27, 2020

excellent experience

By ANMOL B 1

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Jun 4, 2020

Great way to explain

By PARK J

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Oct 29, 2019

very easy to access,

By Chandan K M

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Feb 27, 2019

well crafted course.

By Brian N

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May 20, 2018

Its very refreshing.