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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

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

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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1176 - 1200 of 3,140 Reviews for Machine Learning Foundations: A Case Study Approach

By Jay G

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Jan 10, 2020

This course would have been better if the assignments were in python.

By Zhaokang P

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Sep 29, 2017

it is very practical and benefitial. I like it very much ,thank you~!

By Stephen A

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Aug 20, 2017

Loved the course. Will be back to finish the specialization later on.

By Brian M

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Jul 4, 2016

Just the right level of information. Challenging, but not impossible.

By Abhishek R

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May 28, 2016

Fantastic course by the coursera and professors are teaching awesome.

By Ronny M

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Mar 23, 2016

Easy to follow, yet advanced techniques simplified by great software.

By Felipe N M

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Feb 8, 2016

Great introduction to Machine Learning with nice hands-on activities!

By Jatin S

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Dec 19, 2015

Good teaching your course helped me to learn basic ml algo.

Thank you,

By sridhar s

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Nov 28, 2015

I am in my third week.Its really amazing as I progress in the course.

By Mahipal M

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Apr 30, 2020

The case study approach is really easy to understand . A good start.

By Nikhil C

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May 14, 2019

One of the best machine learning course to start with as a beginner.

By Brandon M

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Sep 12, 2018

A much better introduction to ML compared to other MOOCs I've taken.

By Han W

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Aug 23, 2017

I've learned a lot about machine learning and graphlab, many thanks.

By Sarah W

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Jun 14, 2017

Awesome course! Great overview of ML, very accessible and practical.

By Julien B

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Jan 5, 2017

Dur a suivre en anglais mais très intéressant et très bien structuré

By Theo L

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Sep 15, 2016

A solid introduction to ML. Note this a basic, foundational course.

By AP

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Mar 20, 2016

Easy introduction aimed at beginners, with good content and practice

By Marcos B C (

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Jan 12, 2016

Great course, with very interesting examples and clear explanations.

By Assma A

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Nov 15, 2020

Thank you very much. I really appreciate your kindness and smile :)

By TUSHAR A

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May 25, 2020

Amazing course that taught me a lot of concepts i don't know before

By Kireeti C

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Apr 25, 2020

Really helpful for many students ,lucky to get this course for free

By Jacob W

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Jan 14, 2020

Excellent course! Very well laid out and explained in simple terms!

By S. M

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Nov 6, 2019

nice course. It gives an overview about ML and using Python for it.

By Paulo B S

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Nov 14, 2017

Excelent course. Carlos and Emily are brilliant in their trainings.

By Luiz A

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Aug 13, 2017

Excelent and the enthusiams of lecturesr and helpers is contagious.