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

4.6
stars
13,374 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

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.

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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2476 - 2500 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By KESHAV M 1

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Jun 26, 2020

This course has provided a very good insight of what are the tools that are applied in machine learning and how fast they are from the already existing tools.

By Luis G A P

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Dec 3, 2018

Good introductory course, some videos require more than one time to review them and even doing external research on the topics to understand well the concepts

By Michael S

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Feb 15, 2017

The lectures are substantive, although they tend to be too short in duration. The python application component is very applicable to work-related situations.

By Briana S

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Sep 21, 2016

Excellent course, very charming professors, accessible even for people with limited math/coding backgrounds. Some course material needs to be updated, though.

By Shivani D

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Oct 16, 2020

The course was wonderful. Had a great time learning with doing hands-on on real-time data. But there could have been a more detailed algorithm's explanation.

By SIVASHANKAR S

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Jan 11, 2019

The fundamentals of coding and machine learning concepts are taught in such a way that even a person with no background in computer science can grasp easily.

By Jin C

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Dec 22, 2018

really interesting and helpful to fresh, however it's little bit easy for who have learned something about machine learning and the experiments are too easy.

By José D d O F

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Aug 6, 2017

Provides a good very basic but broad view of the subject. Lectures are really good, but lacks written material and programming assignments are way too basic.

By Roberto S

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Dec 27, 2016

Very interesting course as the start of something that will get more and more interesting. I am happy that I got this specialization and not only the course.

By Volodymyr L

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Feb 2, 2016

I found here all I need for first step in ML. Thank you. But Assignment you should make different. I mean varied tasks but not the same for each new attempt.

By Mohd S

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Mar 9, 2022

Everything is perfect except the library used which is turicreate is not as per current industry requirements, instead Pandas and Scikit learn can be used

By vikash k

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Jan 30, 2022

Great course. Really loved the content. But would have been better if they would have released the last two courses. Basically a finishing touch is missing.

By Ayush G

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Feb 19, 2017

The course is a very good introduction to the specialization. It'd have been better if the course used an open source tool instead of a proprietary product.

By STEFANO C

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Jul 3, 2016

A different version of graphlab was used without I think being duly noted, some functions work differently in newer versions. For the rest it was very good.

By Amit H

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Jan 12, 2016

Effectively introduces to the basics of all different machine learning algorithms through case studies. The recommended tools are easy to use and understand

By Niklas F

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Feb 24, 2017

Really good overview and easy start into the machine learning community. Only point is, that they do not use the usual python packages for machine learning

By S M

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Sep 23, 2016

Overall the course had a good mix of depth and breadth. There should be examples in the presentations and notebooks to help drive home the concepts better.

By Ahmad H

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Jun 26, 2020

A great approach to get started with machine learning, focusing on the big picture first and then, delving into the intricacies in the subsequent courses.

By Devon D

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Sep 10, 2018

Good course I'm just not a fan of graphlab because I think pandas is used more in industry, but the concepts and mini projects were great and challenging.

By luca d f

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Jul 12, 2016

Really exciting and amazing. I liked the approach of starting with case studies, it makes the full understanding of the entire course easier and concrete.

By Salomon D

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Jun 30, 2018

Great overview of approaches in ML and large topics. Recommend to have python and some data analysis practice beforehand to get through material quickly.

By Rune R

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Mar 27, 2016

Very good though a steep learning curve at the last lessons - inspiring lectures as well as practical cases :) Looking forward to the next ML courses :))

By Zacharias V

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Feb 18, 2016

One of the assignments had wrong answers for some of the questions, which made passing it a bit tedious. Other than that, it was a fairly descent course.

By Cassie W

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Oct 23, 2017

Some of the links in this course no longer work and need to be updated. This does effect the assignments. Other than the links not working it was a good

By Patrick A

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Jan 29, 2017

Very good introduction to machine learning with good examples.

Some questions in the quiz could be rephrased to avoid multiple possible interpretations.