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

4.6
stars
13,543 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 2, 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.

SS

May 18, 2020

The course was very informative but I face a lot of problems in installing Graphlab and Turicreate. I request the Mentors please use the Pandas data frame in place of SFrame. The mentors are cool.

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2826 - 2850 of 3,159 Reviews for Machine Learning Foundations: A Case Study Approach

By Tushar A

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Jul 13, 2020

This is a nice course..

By Fernando S

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Aug 20, 2017

Easy going, very good!!

By Godwin

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Jun 4, 2017

Very interesting :) WOW

By Dr. A I

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Jan 3, 2016

This is a great course.

By Mayur S

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Jan 18, 2017

its good, if new to ML

By Shikhar S

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Dec 8, 2020

Great course to start

By Wridheeman B

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Jun 30, 2020

It was a great course

By Eric S

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Jan 5, 2016

Pretty good, overall.

By Mahajan P J

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Dec 26, 2019

The course was good.

By RICHIK G

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Jul 11, 2019

computer vision best

By Pieterjan C

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Oct 2, 2017

very useful to start

By Shreeti S

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Aug 16, 2017

Good to start with.

By Waquar R

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Aug 8, 2016

this is really good

By vivek a

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Apr 18, 2016

Enjoyed this class.

By Fei F

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Dec 22, 2015

Easy for beginners.

By TALHA J

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Aug 29, 2021

it helped me a lot

By Joydev H

•

Nov 15, 2019

Awesome Experience

By Binil K

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Jan 10, 2016

Really great one!!

By Hiếu N Q

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Dec 28, 2015

Good for ML newbie

By amit d

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Feb 3, 2020

nice explaination

By Arnab N

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Jan 4, 2020

Very nice program

By Rahul S

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Dec 19, 2020

GREAT EXPERIANCE

By SURUTHI T

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Jul 5, 2020

more informative

By Oscar M

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May 29, 2016

Very insightfull

By Tulasi P D

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Jul 14, 2020

it is so useful