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

4.6
stars
13,546 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

CL

Oct 7, 2019

This was good introductory course with challenging programming assignments that expanded and grounded the lecture materials. The forums also proved great support when needed, overall very satisfied.

MK

Jul 20, 2019

A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!

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2676 - 2700 of 3,160 Reviews for Machine Learning Foundations: A Case Study Approach

By Sachin R G

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Jun 13, 2020

Need some improvement like much more focus on statistical concepts behind program

By Shashikant K

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Jun 9, 2020

This is very good course. This is helpful for me. Some problem on using graphlab.

By Mich

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Jul 1, 2021

The course needs to update the laboratory files, since the commands are outdated

By Anurag G

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Jul 22, 2020

Preety good course but instead of Sframe , i prefer pandas and sklearn libraries

By Durga P S

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Sep 9, 2018

Very nice foundation course in Machine Learning especially with GraphLab create.

By Henrik

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Jul 2, 2016

Very nice content but dont like we use graphlab since i wont use it after course

By Liebesakt S

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Mar 28, 2016

Last module on Deep learning is not explained well as compared to other modules.

By Xun Y

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Sep 8, 2018

great introductory course to machine learning, includes almost all the aspects.

By Zynab S

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Jun 30, 2016

very good for one who has no idea about machine learning , but I dont like dato

By Bruno K

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Dec 12, 2015

very nice! A little bit more of reading material would be interesting, though..

By MUBEEN M

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Jan 29, 2021

hands on material is overly simplified perhaps because it is foundation course

By Ankita S

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Oct 14, 2020

Great course !! With practical knowledge and the trending topics are captured.

By Mrutyunjaya S Y

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May 16, 2020

It given more understanding of all concepts..Its really helpfull for beginners

By mikhil i

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Dec 1, 2016

The deep learning part of the course needs to be better done. The rest is good

By Ricky W

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Feb 10, 2016

Very nice introduction to Machine Learning and to Python programming language

By Max D C

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Aug 23, 2021

id like to see more examples and use others packages different to turicreate

By Daniel B S d S

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Nov 2, 2016

The course is great, but it would be greater if used open source free tools.

By Igor S

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Apr 13, 2021

I would improve questions in the quiz, sometimes they are really confusing.

By Bilal S

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Oct 17, 2016

It' a fine beginner's course. I liked the hands-on approach using SFrames.

By Marco P

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Dec 4, 2015

The homework assignments were not really about having understood the course

By Sourabh K

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Jun 30, 2020

numpy and pandas are more preferable, but the overall experience was good.

By George B

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May 17, 2018

Pretty great course. Really enjoyed it and looking forward to new courses

By Jeffrey v S

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Oct 31, 2017

Content is good but the delivery is somewhat awkward and chatty at times.

By Brennan W

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Feb 4, 2017

Was a good intro to different kinds of ML. Wish we had used SciKit-Learn.

By Nandan S

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Mar 15, 2018

very good overall. The last week (Neural networks) is a little too fast.