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

4.6
stars
13,492 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

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

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.

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2801 - 2825 of 3,143 Reviews for Machine Learning Foundations: A Case Study Approach

By Mazen A G

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Oct 9, 2016

the best introduction for ML.

By Rishabh C

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Jul 23, 2020

Awesome course to start with

By Rakesh G

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Apr 15, 2019

A good beginners guide to ML

By RISHAB P H

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Apr 15, 2020

add more practical's please

By Mahesh B

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Oct 10, 2019

Good start for ML beginners

By Poornima S

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Feb 18, 2019

It is designed really good.

By Hyeong R J

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Feb 2, 2017

Good lecture and practices.

By Marcos

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Aug 24, 2017

Great introductory course!

By GABRIEL O C D O

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Apr 15, 2021

The course needs updating

By SUPRIYA V S

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Jun 30, 2018

Nice course for beginners

By Vinicius G d O

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Jun 23, 2016

Good introductory course.

By José T G R

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Nov 1, 2015

Very good!!! Excellent!!!

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 4, 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