Chevron Left
Back to Machine Learning Foundations: A Case Study Approach

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

4.6
stars
13,494 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 17, 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

SZ

Dec 20, 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.

Filter by:

1951 - 1975 of 3,144 Reviews for Machine Learning Foundations: A Case Study Approach

By Dan S

•

Dec 12, 2016

Great class!

By sumita m

•

Nov 5, 2016

great course

By Ghassan F

•

Oct 11, 2016

Great course

By Luis D S

•

Jun 4, 2016

Great Course

By clara c

•

May 13, 2016

Great intro!

By Chenxi W

•

Mar 22, 2016

Really good.

By Hans A

•

Jan 31, 2016

Great course

By Ravshan

•

Jan 22, 2016

Good Course!

By rajesh t

•

Jan 13, 2016

Nice course!

By Tianyuan L

•

Dec 29, 2015

Great course

By Josh M

•

Dec 23, 2015

Great class!

By Trevor S

•

Dec 6, 2015

Great format

By Liusicong

•

Oct 12, 2015

Very useful!

By Elamaran S

•

Nov 11, 2024

good course

By Harshdeep S

•

Mar 21, 2022

Nice course

By Dwij J

•

Jan 10, 2022

Good course

By Aqsa Z

•

Jan 9, 2021

Nice course

By SHYAM P J

•

Nov 13, 2020

Excellent!!

By KIM B C

•

Nov 13, 2020

excellent!!

By Abdul Q

•

Oct 22, 2020

good course

By lavanya v

•

Sep 28, 2020

very useful

By Nora T

•

Sep 16, 2020

Very Useful

By Raditya Y A

•

Sep 14, 2020

Thank You !

By Saikamal n

•

Aug 25, 2020

outstanding

By Nithesha K

•

Aug 24, 2020

Good course