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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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2626 - 2650 of 3,156 Reviews for Machine Learning Foundations: A Case Study Approach

By Hitesh D

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Oct 1, 2019

The course has helped me understand basic of Machine learning and has created interest for me.

Thanks :)

By Pushpak T

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Mar 13, 2016

Gives a high-level understanding of machine learning concepts and focuses a lot on its application part.

By Rajkumar D

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Mar 2, 2016

Good Start up with case study approach to just understand what we gonna learn in further specialization.

By ashish

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May 2, 2018

The course from coursera was well delivered. Albeit, their seems to be too much dependency on graphlab.

By Mohit A

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Nov 12, 2019

Great learning experience! We should have option of videos with exercises using Pandas & scikit learn.

By Michele P

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Sep 5, 2017

Good introduction, although the implementation exercises use mainly GraphLab which is not open-source

By Santosh W

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Feb 16, 2016

Useful course. Covered good amount of usecases for Machine learning concepts with handson experience.

By Cristian C H M

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Jun 20, 2020

The videos are out dated and Turicreate doesn't work on Python 3.8 and the latest version of Ubuntu.

By Jeff L

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Nov 29, 2015

This was a fantastic course for someone both unfamiliar with machine learning algorithms and python.

By Tomáš Z

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Nov 30, 2018

Nice intro but it could have been more in depth, not just as a simple graphlab/turicreate tutorials

By Jonathan E

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Aug 15, 2017

I liked the interactive python programming however the course could be more rigorous for my tastes.

By Sergey G

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Mar 23, 2019

It'd be great to get rid of showing how teacher type code or write something, it's kind of boring.

By DeepLyrics

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Dec 11, 2016

Great introductory course for beginners to machine learning and I loved the programming tutorials.

By Alfonso M

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Mar 26, 2016

Instructors are clear and passional about their teaching. I wish the course was slightly deeper.

By E. M S

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May 17, 2017

Good overview and programming warm-up. Just need to change the links to turi.com from dato.com

By Laura M

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Feb 15, 2023

I would prefer the use of other tools better than turicreate , but the concepts are very clear

By SANJEEV K V

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Mar 17, 2021

Nice Course and developed a nice understanding from the questions present in the assignments.

By Tomas O

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Nov 8, 2019

It has a lot of problems with the programs you should install. But the content it's amazing.

By MICHAEL G

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Nov 14, 2017

This course was a very good introduction to some of the techniques used in Machine Learning.

By Stefan S

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Jan 4, 2016

Good intro to topics in machine learning as well as to Graphlab Create and iPython Notebook.

By Stanislav B

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Apr 18, 2020

There were unnecessary problems with data. Last course (Clustering and retrieval is better)

By weimin l

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Oct 27, 2016

A very interesting course! I learned a lot. Will continue on the next course once it ready.

By Rishabh P

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Jul 26, 2020

It would be great if you start deployement of things in python also not only in turicreate

By Brent R

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Jan 29, 2017

Good intro to ML, but would've enjoyed less of the "Black Box" approach in using Graphlab.

By Aaron W

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Jan 1, 2016

Great introduction to ML concepts! I'm hoping to dig a little deeper in the next courses!