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

4.6
9,116 ratings
2,175 reviews

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 20, 2016

Great course!\n\nEmily 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 19, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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251 - 275 of 2,093 Reviews for Machine Learning Foundations: A Case Study Approach

By Raphael K

Feb 29, 2016

Nice class, give a brief introduction to all the methods use in ML without going deep. If you just want to get an idea of what ML Technics are and how to implement them this course is for you. If you are want more technical details about ML this class is not for you.

By Roberto C

Oct 22, 2015

I really liked this course (I have one week to finish, but I have enough data to judge). The professors are really pedagogic and the examples are really clear. The only thing is that I would rather have more difficult programs, I feel like that I do not have much to think to pass the tests.

By Hansel D

Nov 15, 2015

I consider the course is easy to understand and useful

By Chris W

Nov 16, 2015

Content and delivery are both excellent. I would strongly recommend this course to anyone looking to get started in machine learning.

By Mithilesh K S

Dec 10, 2016

One of the coolest approach to machine learning by using real life data and its example. I loved it as a beginner to machine learning.

By SHYAM S K

Sep 29, 2015

The Best Thing About Case Based Study is That You See The Things Actually Happening And Nothing Feels More Good Than That :))

By Tim J

Jan 09, 2016

Excellent overview course. It has exactly the right balance between explaining Machine Learning concepts, and providing enough supporting mathematics & logic to understand why these concepts are correct (without going through epsilon-delta proofs).

Having followed several Machine Learning courses, this is now definitely my favourite new course, replacing Andrew Ng's famous course here on Coursera (which was also very good & especially complete, but required too often a leap of faith - this course provides really more details on the "why"). Furthermore, the exercises in this course are spot-on: they use Python and GraphLab Create (for which you get a 1 year student license when taking this course) - the big advantage is that you can focus on the Machine Learning aspect, and not on how to implement something in Python (or Matlab or R). The exercises are challenging enough and require some thought, exactly what they should do. This is not a "look up the right answer in the slides" course when it comes to exercises, which I particularly like.

The chemistry between the teachers is also very nice and shows they just love Machine Learning, and love teaching it (which they do very well).

If you some familiarity with statistics (a bit) and mathematics (a bit of matrix & vector calculations), and want to understand what Machine Learning is about, then this is THE course for you.

By Javier P

Oct 28, 2016

It was a great course. I learned many things during this course. Btw, the teachers are really cool... super cool.

By Dheeraj A

Oct 28, 2016

Course combines Real Word Applications with simple implementation via IPython Notebooks. Professors

know their stuff but are super chill. Pretty good assignments and quizzes.

By Theodore G

Oct 23, 2016

A really interesting, introductory course in Machine Learning Methods and their applications. The case study approach followed by the instructors makes it ideal to learn how these ideas used in real-life problems. The programming language used is Python (GraphLab Create or Open Sourced libraries), which is most probably the best choice for newcomers in the field.

By Patrick M

Feb 01, 2016

A fascinating tour of what's possible today with modern machine learning tools. The beauty and challenge of this course is the approach - diving right in to the tools to work through and experiment with some case studies. This is not a talk and visuals only course. You will be hands on.

This may be demanding for some, but is worth the effort. The course says no previous experience necessary in Python, but I recommend having at least completed a beginner's course before trying to tackle this. (Or familiarized yourself with Python if you have other programming experience - it has its quirks, like every language.)

The course will introduce you to the current state of play in machine learning and both show you what's possible and also where the limitations are. This is not a superficial course (talking points only) - you will learn enough to be dangerous. If you want to be a little safer, do the follow-on courses too. (At this time, only the 2nd course has run - regression - but it was very good).

By Bowen C

Oct 05, 2016

I love this course. I do expect we can learn more about the algorithms though. Overall I love this course

By Nguyễn T H V

Aug 07, 2016

Awesome!

By Daniel Z

Mar 08, 2016

This course is a piece of work. It is really impressive the cases study approach presented

By Ayush S

Jul 26, 2016

Amazing Course. Covers the fundamental clearly. Best machine learning course out there. Expects you to do some research. A good course in all.

By Jesse W

Jun 16, 2016

Great class, definitely worth taking. I have worked on Machine Learning in the past and it helped me greatly. I am looking forward to all the courses.

By Abhijit K

Jun 02, 2016

Very nice way of teaching such a difficult subject. I like both the instructors. Assignments are bit easy though and must have been on open source software.

By Satish M G

Dec 09, 2016

This is an excellent course provided by the creators of this course. My sincere appreciation to both of them. The length of theory and practicals are very appropriate. I am very sure to continue all courses and finish them and master them. Thanks coursera for providing this course.

By Oluseyi

Jun 19, 2017

I enjoyed the occasional humor that Emily and Carlos add to the lecture.

By Tommy W

Oct 19, 2015

Very good!!! Super helpful

By Balaji C G

Jan 09, 2017

The case study approach for explaining machine learning concepts is commendable. This kind of approach will not only help in cementing the concepts but helps in making decisions when it comes to real-life applications.

By Daniel M

Dec 23, 2015

I love this course. I found it informative and the materials easy to understand.

By Alberto V

Jan 14, 2017

A very good introduction to foundations of machine learning. The learning methodology based on study cases is amazing and gripping and the ipython notebooks used in the practical sessions are very instructive. Strongly recommendable for everybody who want to start in this world

By Mao D

Dec 04, 2015

very nice course to have, hope they can provide sample code for homework after submission.

By Pablo C

Dec 29, 2015

You learn the basic concepts in a funny way, what else can you ask for?