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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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2451 - 2475 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Shekar T

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Feb 3, 2016

Very nicely done course.

I have to note that I was not sure how to get help as I was stuck on a issue. I was able to finally figure out but the instructors are unresponsive :(.

By Julien L

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Dec 16, 2020

Nice approach, I liked the balance between theory and practice.

Great use of quizz to force assimilation of new concepts.

This is a brilliant introduction to Machine Learning.

By shashi

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May 24, 2017

Very good course. Course modules are easy to follow because of the hands on approach.

But just wondering why the capstone project is removed from the course specialization?

By Patrick B

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Nov 4, 2016

I love what I am learning in this course however some of the lectures are completely disconnected from the quizzes. They could do a better job quizzing based on the lecture.

By Srikanth R K

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Apr 24, 2016

This course has given a good introduction to the broad topics of ML and what I will be taught in the next 5 courses in the specialisation and gave me a reason to be excited.

By Tapajit

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Mar 25, 2018

Really good and easy going course. It gets us familiar with a lot of useful techniques in ML, which surely will be explored deeper in further courses in the specialization.

By J N B P

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Sep 29, 2020

You can get the overall idea of how different algorithms perform using inbuild libraries. You will not have to learn the underlying mathematical concepts for this course.

By Samer

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May 19, 2018

It provides a good general insight into different implementations of machine learning.

The only downside is the proprietary and commercial licensing of the used algorithm.

By Rattaphon H

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Jun 12, 2016

This course start from problem. So this great to motivate the content. However, there are lot of confusion questions that lead to miss understand the exercise problems.

By Aswin

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Sep 30, 2016

An awesome course to get a basic idea about various machine learning techniques. Would've been great if instructions for scikit learn were also provided in the videos

By Oren Y

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Nov 12, 2016

Very clearly explained, easy-to-follow pace and great examples.

would have been absolutely great if there were references to do the same exercises using pandas/scikit

By Dong D

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Jan 15, 2016

Great Course, I really enjoy it. Compared with other machine learning courses, I think this one is really a good start, for its using of examples close to our life.

By Yusuf J

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Nov 22, 2015

Last section was tedious. Access to a year long graphlab licence was nice though especially since I graduated as an undergraduate in 2014 and am no longer a student.

By rafael c

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Jan 18, 2016

Very good course since it give you a high level view on Machine Learning. It would had been useful to have complementary readings and examples using other libraries

By Saida Q

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Jul 30, 2022

Very much interesting and deep. Gives hands on experience with real life examples and with basic knowledge. I would definetly recommand this course to new learner.

By Cory A

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Mar 23, 2018

I really enjoyed the material. I think the course should focus more on open source tools. Some of the quizzes are rather badly worded and could use some retooling.

By Dorian K

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Mar 15, 2016

The course is great, you do learn a lot of the general concepts. I wish they would use general python packages instead of graphlab, but this is not a deal breaker.

By Owen M

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Feb 6, 2016

The course was taught well, however I thought there could have been a few more videos with more details. Although I understand that it's really an overview course.

By Fearghal O

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Feb 5, 2016

Really interesting course that gives a great introduction to Machine Learning. Might be a little too much for someone with no programming or statistics experience.

By pradyut n

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Jul 18, 2020

Libraries are old and no support . Though I somehow worked it out.

But man explanations are so good and the professors are nice too.I like how they make us laugh .

By Andrei N

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May 27, 2020

Really good introductory course. Sadly the code in the video is not up to date and there are points were the questions are quite vaguely formulated in the quizes.

By Vishakha V

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Apr 10, 2018

The course is great for beginners, I would have appreciated a more detailed explanation of statistical models used rather than using readymade graphlab functions.

By Sachin P K

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Mar 2, 2020

Good for the beginners to learn Machine Learning and Neural Networks in detail. This course also includes hands on sessions/ assignments which helps in learning.

By Parijat R

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Feb 27, 2018

Many basic ML concepts were touched upon - the assignments, especially in Parts 1-4 were very easy and required very little brainstorming :) - hence a star less.

By Vladimir G

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Oct 26, 2017

The course is very comprehensible, even for not so technical people. The materials are good and the lecturers are able to explain complex concepts to everyone.