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

4.6
stars
13,527 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

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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901 - 925 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Vaibhav G

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Jul 9, 2017

It was very nice. However the iPython assignments became a little redundant and mechanical after a while.

By Bowen C

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Oct 4, 2016

I love this course. I do expect we can learn more about the algorithms though. Overall I love this course

By Md. T H

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Jul 13, 2016

Basically an ML - 101 course. Certainly it is one of the best possible introductory courses on any topic.

By Aku-Jaakko S

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Mar 7, 2016

Hands down, one of the best courses I've ever taken. Exciting, informative and motivating teaching style!

By Neta Z

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Oct 23, 2015

Thank you!

Course instruction was clear and home work was challenging enough to make me have to listen ;-)

By Dhatri P

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Apr 10, 2020

The course is very well designed along with python explanation for ML and explanation is very excellent.

By Ritu R

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Oct 5, 2019

Very helpful course, it helps to understand how to use your machine learning concepts in the real world.

By Xuezhou L

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Jan 8, 2018

This is the best course of Machine Learning I have taken part in. Thanks for the teachers and coursera !

By Sameer C

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Jun 25, 2016

A really good course for beginners who want to understand the concepts and not the underlying algorithms

By Alexei A R

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Feb 29, 2016

Excelente curso, lástima que no cuente con los requerimientos mínimos de internet para pasarlo. Saludos.

By Hung W K

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Oct 22, 2024

Both instructors teach good and clear. It is enjoyable to pick up turicreate by doing the lab exercise.

By Sai K

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Jul 10, 2018

Very good learning experience. Provides a basis to understand the various models for various scenarios.

By Sandhya S

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Jan 19, 2018

My favourite ML course!! Really well structured, simple explanations and the instructors are brilliant.

By Sreten M

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Feb 19, 2017

Bravo, bravo, bravo.

I'm really enjoyed this course. I'm learned what is behind "ML". .

Great teachers.

By Rajat S B

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Mar 17, 2016

Great, It clears what we will learn and what our approach should be during the specialization.

Thank you

By Deepak S

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Jul 25, 2020

Excellent course. Case study approach seems to be more interesting than simply concept based approach.

By Varun S

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Sep 19, 2019

Good explanation on the various topics with clear hands-on sessions. Great course to get started on ML

By Marco R

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Jul 4, 2017

Easy to follow and a lot of usefull information that really gets you excited for the upcoming courses.

By Dale W

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Mar 17, 2016

I love these two guys, since they can convey their ideas vividly with big smile, which is pretty cool!

By Juan D R G

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Jan 3, 2016

This course really gives you the foundations using great examples. It's a good place to start learning

By João S

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Dec 20, 2015

Very nice intro to the topic of ML.

Fun videos, nice examples, good approach to teaching complex topics

By Ridhwanul H

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Oct 16, 2017

A really great course. Enjoyed how various topics were introduced in a simple, beginner friendly way.

By Pedro P

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Feb 6, 2017

would be perfect if it didnt lie to me saying there is a capstone project and courses 5 and 6, i wish

By Omar N T

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Mar 30, 2016

All thanks to Carlos and Emily.

This course is great for knowing what ML is with practical examples :)