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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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2326 - 2350 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Pritish K

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May 20, 2017

Overall nice refresher course. Some of the material was basic.

only downside is that you have to use DATO for the exercises. Different courses have their own requirements, but possibly giving people the option to do this in R or regular python owuld help. Having an optional model with dato where the benefits are shown would be nice.

By Dillon D

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Sep 4, 2016

Very informative and make the machine learning experience much easier for a beginner to all these new concepts. This course is very well set up to help students into the future apply there new knowledge. Only thing is the software was a little difficult to at first get working on my mac but other than that everything was fabulous.

By Mohamed G M S B

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Sep 2, 2018

I would've preferred if the used tools were opensource. Also, I felt that in many videos I lost my concentration due to the side comments that had nothing to do with the actual technicalities of the course. Nevertheless, the material presented in this course provides an excellent overview for the foundations of machine learning.

By Igor B

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Dec 29, 2016

The course was very well taught and the exercises provide a realistic introduction into real-world problems. The only thing that is missing to get to a 5-star rating would be to use standard machine learning libraries (scikit-learn, which is free) instead of GraphLab Create, which requires a paid license to be used commercially.

By Vijay V

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Jan 29, 2016

Great Introduction to Generic Machine Learning Concepts.

One suggestion to the teachers would be to include an optional programming section just to introduce GraphLab to users. There is a lot of API calls which are explained on the go but a high level view of the library with the relevant structuring of APIs would be helpful.

By Abubakr M S

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Nov 11, 2018

This course is very informative and useful for anyone who have no machine learning background. The case study approach helped a lot in understanding the core of every concept before deploying.

The only drawback was that there was no tutorial on how to install the software which was so tricky and take me ages to install it.

By Sparsh K

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Sep 1, 2020

The course is pretty decent but what i really didn't like was it outdated use of software and pretty less efficient mentors.I suggest, to please moderate this course, this course is indeed a good one but need to be supplied with new references and less dependent on particular libraries.Otherwise the course was great.

By Jarred N

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Nov 23, 2015

I think the course met my expectations – it's super high-level and does not at all go over the underlying algorithms involved. I give it 4 stars because I have this feeling like this specialization is an underhanded way to sell the Dato GraphLab Create product. There's a bit of a conflict of interest going on here.

By Kumar N

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Jul 30, 2020

Wass a great introductory course. Definitely recommend for starters. The course was well constructed and presented. The only problem I faced was from the software side. I was having a hard time installing and importing packages, those are not covered in this course. I like the case study approach as an introduction.

By Elena I

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Nov 25, 2018

The course has everything you need to get an overview of machine learning. It's perfect to understand the purposes and techniques used. However, I'm a bit concerned with practical tasks, since they heavily imply on GraphLab create, and this is a serious disadvantage, since one will barely use it in future.

By レンユー

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Jul 22, 2018

This course is great if you just started getting into the field of machine learning. (Great if you have no or limited programming background)

Pace is a little bit slow and Programming assignments does not captures algorithms discussed in lecture.( Although it mentions, it never let you implement yourself.)

By Lennart B

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Feb 7, 2016

Very good introduction to machine learning, quickly enables the student to perform regression, classifications, etc. but it would be nice if the course went into a little more detail, the quizzes are very superficial. It would also be beneficial to explore examples of applications across different fields.

By Forrest G K I

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Aug 15, 2018

I enjoyed this class. It does provide a good over-view of the different machine learning algorithms and their practical applications. My only qualm was that the programming assignments seemed somewhat irrelevant as the underlying structure of the different machine learning algorithms had not been taught.

By sarathva v

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Nov 11, 2019

Nicely covered basic ideas about different areas in ML . Hans-on sessions gave a very good idea to solve ML problems practically. Theory explanations where good.

One suggestion i had is about tool used it would have been cool if course was with scikit learn and pandas, since many companies use the same.

By firstin l

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Feb 12, 2017

I really enjoyed the overall materials and especially loved the way they split the course into two sections:Theory and Programming.

However, i wish they were using more standard packages such as pandas, or skit-learn instead of graphlab. It was a good class to taste what is going on in the world of ML.

By Michael R

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Jan 18, 2016

Great introduction to machine learning concepts with nice assignments. It seems there needs to be some cleanup performed so that the lectures and content match up a bit better. Overall a useful and approachable course to motivate the need for additional study in the rest of the "specialization."

By Jane z

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Jan 8, 2020

I really enjoyed this hands-on course with a lot of practice. The difficult part was the week 1 when we had to set up the virtual environment, and pass the first quiz. I believe that if there is more support at the beginning, more people would have stayed on to finish the course.

Thank you!

By Andrey Y

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Dec 28, 2017

Assignment instructions are not very clear and often not formatted properly - multiple questions are "glued" in a single block. It would be good to spend more time on GraphLab API at the beginig of the course. iNotebook did not work on coursera.org, I had to install a local version of Python.

By Wenersamy R d A

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Apr 8, 2021

The course has a really interesting approach, and I have enjoyed it, but as Turicreate (previously Graphlab) has not become a mainstream tool (on top of the difficulty to use it with Windows), I would rather have also some exposure to other tools, as Scikit-learn and Tensorflow, for example.

By Ayush K

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Sep 2, 2018

Case study approach is really helpful but we need to understand the formula behind those deep learning scores which i think has not shown in this course. Second, if you can provide more videos for coding then it will really helpful to do the "Programming Assignments" which i think is tough.

By Nick B

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Jan 29, 2016

Well designed and executed in the main. As videos are recorded once but viewed thousands of times it would have been nice if they had taken more time to write a script and look more professional - also mistakes in the videos that don't match the current material are few, but very annoying.

By Rohit K

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Nov 16, 2016

Good Course for someone wanting an overview of techniques. You would not be building something very cool after this. Course assignments are not very challenging, some good questions must be included.

Some mathematical part should be there. Expecting it in other courses in specialization.

By Mascha L

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Jan 24, 2016

I found this to be an excellent course. Both the instructors are excited about the work they are doing and do a good job of teaching the materials. I don't have a statistics background and college calculus is more than a little fuzzy. I was still able to understand most of the course.

By Manish S

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Jan 15, 2016

Pros:

1. Practical and hands on approach

2. How multiple problems like prediction, sentiment analysis, text retrieval etc can be mapped to a common ML model

Cons:

1. Uses DATO toolkit which is a licensed tool.

2. Maths behind any used technique is not discussed, which I think is an issue.

By Sunghyun H

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Dec 29, 2015

Very nice for those of people who want to learn the concepts of machine learning. However, libraries that used (which are not pandas and scikit-learn) was not satisfying for me. Googling about those libraries was totally difficult while there are lots of documents using pandas and scipy.