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

4.6
stars
13,485 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

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.

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.

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926 - 950 of 3,140 Reviews for Machine Learning Foundations: A Case Study Approach

By Da-Rin Q

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Oct 30, 2015

A great course if you want to learn some basics in machine learning and where/how it can be applied.

By Sadat B F

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May 3, 2020

I really enjoyed the course . Have learnt a lot of fundamentals of ML and Really helpful for futute

By Kowshiha P

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Dec 28, 2016

Very well taught, concepts clearly explained, a good introduction to the world of machine learning!

By Li D

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Dec 26, 2016

This course is quite advanced. You should do this after you have finished the one from Stanford U.

By Bilkan E

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Aug 17, 2016

Awesome course! Very helpful with a practical / example-driven approach that helps build intuition.

By mithun g

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Jan 28, 2016

A very nice approach to learning Machine Learning. Doesn't scare you with lot of technical jargons.

By Wenqi Z

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Jan 16, 2016

The programming assignment really put the learning into practice. It's very practical and hands on.

By Zhubin W

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Jan 10, 2016

这门课是系列课程的第一门，比较基础，我是在上完Andrew的Machine Learning课程后上的， 通过这门课程，开始逐渐习惯了利用Python语言处理相关模型的方法,我会继续跟进系列课程的。

By Vinay S

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Jul 6, 2020

Too awesome course i really enjoy it, teaching way was too good,and teacher also. Thanku Coursera!

By kamrun n

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May 4, 2020

I had BEST learning experience in this course. It is very easy to understand and to implement too.

By Almir I

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Mar 5, 2019

Great course. Very clear and detailed presentation of concepts and techniques of Machine Learning.

By Bigyan S

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Nov 18, 2017

Just what I needed. Goes through the applications first and then to the theoretical aspects later.

By Asif K

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Nov 16, 2016

Good course to build concepts of machine learning that is good platform to move to advanced level.

By Zhiming L

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Oct 14, 2015

The course is very basic, and I learned a lot how to write Python code and using the GraphLab tool

By Harshit G

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Aug 11, 2020

Condensed and rich information delivered through informative videos and useful jupyter notebooks.

By Chalapathi R K

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Dec 8, 2019

I learned fundamentals of Machine Learning. I enjoyed learning with good content of videos and do

By Shivam G

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Sep 14, 2018

Very well designed course.

Emphasizes more on application side and covers primary domains as well.

By Caio V

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May 9, 2018

Proud of seeing a Brazilian at this level!! And really excited to the coming lessons! Thanks all

By Federico H

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Aug 25, 2019

Very good intruction to machine learning and AI, with a very pragmatic approach. I recommend it.

By Durai M

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Nov 21, 2017

The course is very intuitive and assignments are very helpful to understand the concept in depth

By Brian F

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Oct 13, 2016

Very clearly explained and practical machine learning use cases presented in a welcoming manner.

By Sourav D

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May 8, 2016

Learnt many new concepts and is amazed by getting to know how machine learning is actually done.

By Oliver W

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May 3, 2016

Absolutely love the light hearted approach and am having a great time learning something I love.

By Rajib M

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Mar 14, 2016

SFrame & GraphLab tutorials were great, Really liked how it was presented .

Loving this Course !

By Poonam H

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Jul 18, 2020

It was a very helpful course. I got to learn many new things and also I learnt a new subject .