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

4.6
stars
11,918 ratings
2,854 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

PM
Aug 18, 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.

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

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2051 - 2075 of 2,770 Reviews for Machine Learning Foundations: A Case Study Approach

By Muskan G

Sep 24, 2020

The installation process of jupyter and ipython notebook can be more clearly explained because many people including me had a tough time figuring out the right way to install and get started with the assignments. Many people are stuck there only for 2-3 days and give up the course.

Otherwise I enjoyed the course with the different approach of teaching(the case study approach)

By Göran

Apr 28, 2018

Very good course and inspiring case studies! A small fix needed is to change the order of the concluding remarks of each week and the Jupyter Notebook exercises. In the film you always refer back to the Jupyter Notebook exercises, but when I look at the film (following each week very linearly) I have not yet done the exercises. Thank you for a fun and interesting course!

By Jesse Z

Aug 9, 2016

I really enjoyed the intro, I would suggest that you take a bit of Python, and Brush up on your Calculus, because you will be needing it. It's possible to keep up conceptually, but I barley made it, and am taking some Python, and Calculus to make sure I'm not waisting time learning skills that are needed in the course, and can focus on the material being presented.

By Ivan T

Jun 20, 2017

This is an awesome course for those who are curious of what machine learning is all about and want to get a broad overview. You don't really have to have programming background, just some basic math will suffice. However, if you look at some background and practical implementations, Andrew Ng's course is the one to go. This one is too shallow to anything practical.

By Mo Z

Aug 8, 2016

The materials are very interesting. Great introductory course. There are a few problems that need workaround if you are using Amazon AWS to complete the quiz. Need some FAQ section for people who are using Amazon AWS. Wish there are some documents reference python/learn. Excited to take the deeper course but need to upgrade the computer instead of using AWS again.

By philip g

Apr 13, 2016

Very good introductory course, especially to someone with little knowledge of ML. My only frustration is that I have some background in machine learning, so not very much new material- I would have skipped this and jumped straight into the the regression models course, but you need to complete them all to unlock the capstone (which looks really, really cool!)

By Qing D

Jan 17, 2017

This course is very practical for starters in Machine Learning. The ML methods it teaches are very fundamental, with clear and intuitive explanations about how and why they work. This course is recommanded to those who want a hands-on learning experience and who are not statistics experts that do not care much about the mathematical proofs of the algorithms.

By Paul H

Jan 19, 2020

Great overview of machine learning concepts. The content is structured in a helpful way to give you conceptual understanding of machine learning. Some of the quizzes and materials are a bit messy (most of the challenge is figuring out what parts are out of date or related to course material that previously existed). For the price, it's definitely worth it.

By Anant S

Jul 24, 2020

The course was really informative but is a little outdated as it uses Graphlab and SFrames which is available in the older versions of Anaconda and Python. It is also a very tedious task to study this course on windows. I had to install Linux on my system to study this course. I gave it four stars because it cleared the concept of ML through Case Studies.

By Balazs K

Dec 24, 2016

In general, a nice "into" style course to show the capabilities of different ML solutions. However, trying to be so "cool", "awesome", and "exciting" slash back easily: the first thing I remember from this course is that annoying squeaking giraffe, and not the real content.... Nonetheless, If you need a practical introduction to ML, its worth the effort!

By José M G A

Apr 10, 2016

Although is a good for a start, and Graphlab framework is state-of-art software. I would like the same content developed using mainstream opensource frameworks like pandas, scipy, numpy, etc.

One of my interest in this courses was that they used python instead of R (which I don't like too much for it's inconsistencies). Python is faster and more reliable.

By Gaurav G

Oct 26, 2018

though it was a great course i was a little let down when module 5 and 6 were taken off the specialization series. A great course for beginners to understand machine learning as it introduced the aspects of it without getting too involved in math so that we could grasp the basics first. definitely will recommend to those wishing to dip their toes in MI.

By Keegan G

Nov 13, 2019

I learned good material but it was very confusing getting started with graphlab create. Supposedly there is a switch to Turi Create, which I received an email that stated '..the content in this course has been updated for Turi Create', but none of the content is updated. I still got everything to work and do feel I got what I wanted out of the course.

By Natasha B

Oct 10, 2016

Great course. Good for a broad overview. If you already know basic concepts like regression, classifiers, etc from a statistics class it might be a little slow. Also, the class is taught using graph lab which is not a free software. If you wanted to try it something else that is free, you could... But you will spend a LOT more time on the assignments.

By Mariel T

Jul 5, 2017

it was great tho the week 6 quiz was quite difficult. a lot of comments in the discussion forum were helpful for me to complete the quiz but lots of feedback suggested improving the lessons to match the quiz or vice versa.

i think it would also help upon submitting the quiz to display the answers you chose , not jsut whether they were correct or not

By Nicolas O

Jan 1, 2016

Fantastic course! Great teachers and very nice to see real-world applications in action. Would have rated 5 stars if an open source library like scikit-learn were used. Students can still use sklearn, but all the examples are using GraphLab Create, a great library, but you need a very expensive (at least for my budget) license to use it commercially.

By Bahram A

Nov 15, 2020

Before taking this course, I read users' reviews, I knew that this course is a bit out-dated and to my surprise, it mostly uses the proprietary library, graphlab, turicreate. But those obstacles didn't stop, I vowed that I'd learn the concepts but implement the exercise and other things using open-source packages, like Panas, Scikit-Learn and so on.

By Manoj K

Oct 11, 2020

Very good introductory course on Machine Learning. Be prepared to dedicate extra time to explore the turicreate API. Overall well packaged quizzes and exercises. I found the explanation of math in some areas (for example recommender systems) somewhat lacking; however there are further courses in this specialization which might cover things in depth.

By Unai G M

Mar 12, 2020

It is a very well structured course and well focused, the idea of the case study approach is great. The only thing that I disliked was the fact that the jupyter notebooks were explained using the library Turicreate, which has been a great discovery, but it is not as widely used as Scikit-Learn. It would have been nice to have both implementations.

By phani k v

Apr 14, 2017

It would be the best staring point for people new to machine learning .The course was very clear and well organized .The assignments and quizzes have given me much deeper understanding of what is being told in the video lectures . The only thing which I felt could get better was using other libraries than graphlab ,libraries which companies use .

By 허웅

Dec 12, 2015

It is great to understand overall machine leacning technique. However, one thing which is not good is we should use dato's product, graphlab almost mandotorily. This product is very expensive, so we would be hard time persuading our company to purchase the license. I think it is much better for course student to have special offer from dato

By Shashank k

May 4, 2020

Good explanation and Great Approach to ML using Case study But Sframe and Graphlab installation is a difficult task. Most of the students do not like this just because sframe files did not work at all when you loaded the data set but doing the right approach can make the work easier and just follow graph lab instructions for installation.

By NGUYEN M T

Mar 16, 2020

Generally, the course provides very helpful machine learning algorithms with hands-on labs. The lecturers explain problems as the beginning stage to machine learning understanding with practical examples. It would be more helpful if there were instructions on the installation of software, such as Jupiter Notebook and Turicreate, in Linux.

By David H

Oct 4, 2016

A great introductory course to Machine Learning for anyone with experience programming. It's presented as a survey of various Machine Learning techniques and I appreciated seeing many motivating examples for the topics covered. The hands-on examples were accessible, but at the same time gave familiarity with real-world tools like IPython.

By Jerry S

Mar 20, 2017

In general it is a good introductory course. The lectures are easy to understand and the learning materials, especially the notebooks, are very useful, but it is a pity to know that the last two courses of the specialization were closed. Most of the programming assignments are too easy(just copy-and-paste), which is another disadvantage.