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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

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.

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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2276 - 2300 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Arun s n

•

Oct 7, 2020

its a basic course gives the overview of everything you need to learn in deep learning. it's would be more better if they teach this in more used library like tenser flow or keras would be lot more better.

ye, one more thing i faced problem with the quiz where you need to write a function to do certain task.it would be more helpful if you mention more clearly what parameters to pass in those function which we need to create .

thank you

By Shiva B

•

Dec 6, 2015

Very good hands-on approach to practical machine learning. However, as the models get more complicated, some of the material isn't really explained very well, specially the last module about 'deep learning' is quite superfluous. I also wish, the course favored providing a bit more mathematical intuition over just graphlab usage. All things aside, if you are really curious about machine learning you should take this course. Be hungry!

By Martin D

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Dec 24, 2017

Excellent overview of important Machine Learning concepts. There were some technical difficulties and reversed steps that were a problem at some points. If you are taking this course you can overcome those problems but I recommend the course creators take their own course to make sure everything is logically ordered and technically executable. :) Overall great course, though (content = 5, technical issues = 4). Thanks!

By Heiko T

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Sep 22, 2018

Course provides very good overview on ML methods and applications - the Python programming exercises are very hand on, in some situations I was missing a little bit of the theoretical background which I than looked up in other sources (e.g. what is the logistic classifier that was used and why is it useful). But of course this was only the foundations course and it provided good basis for the coming specializations.

By Diptiman B

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Aug 9, 2018

Some details / variation about the "features" collection (training / test dataset) i.e. how could we build various "features" to tune our ML algorithm perform better prediction. Though there is a lecture on the impact of "features" on ML algorithm is part of the course; however, it would have been better to look at steps of building various types of "features". For me the "deep features" on images are still blurry.

By Grace P

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Jan 7, 2016

At times, this course feels like an extended advertisement for Graphlab Create, which was founded by one of the instructors. The case studies are all quite interesting, however. I would not recommend this course, except that the following course is awesome and this course is helpful preparation for the regression course. The best part of this course is the interaction between the instructors, who are very fun.

By Xiangning L

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Jun 26, 2020

Emily and Carlos are so cute and they gave pretty clear lectures, I love them so much!!! But the programming assignments given are so simple and easy, and the theory behind the codes are not explained explicitly. Maybe because it is the first course of the specialization, I will take the following courses to see whether they are much harder in the later ones and more useful to improve my machine learning skills.

By Toby P

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Oct 18, 2015

Pros: Really fun to watch. Great introduction to several ML algorithms. Offers specific examples that you can follow along with (Python).

Cons: Uses a proprietary library to demonstrate concepts.

Overall: At first I was concerned if this specialization would be too difficult, but this specific course was surprisingly easy to follow along.

Note, I highly recommend the Data Science specialization here on Coursera.

By Axs C

•

Nov 14, 2016

It seems a very good foundations course, it would be great to dive into more technical aspects, specially because I suppose the majority of us has a background related to maths, statistics, cs, etc. Very useful indeed, hope the studd that comes ahead gets even better. Congratulations. Also, it would be great to learn these methods using other ML libraries, like open source / free ones. All the best, Alexis

By Michael B

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Mar 1, 2016

Great introduction to Machine Learning Specialization and the various algorithms employed in the different courses. In my opinion every Specialization should have an introductory course that covers the various different things that will form the focus of each subsequent course so that students do not start the Specialization and find out down the line that the Specialization is not quite what they expected.

By Nelson C B

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Nov 4, 2015

Me agradó haber manejado con Python herramientas de aprendizaje de máquinas. El curso pretende ser una breve introducción de las principales técnicas y pienso que lo cumple. Sólo me hizo falta que trataran el tema de la creación de las bases de datos, porque siempre se hicieron ejercicios con datos aportados por los profesores, pero no sugirieron la creación de datos propios en el formato de la herramienta.

By Michaël L

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Mar 30, 2016

Very good general overview of ML. I put 4 stars instead of 5 because too much focus is made on Graphlab for the hands on exercises. The focus of this course is not on the tool itself however one should know that this is the primary library used for the exercises. Details are given for those who wants to use other library but the foundation of the course are clearly to present and showcase Dato's product.

By Prasad B D

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Dec 7, 2015

The content and articulation was excellent. Inspired and motivated me to continue the course in spite of my struggle to understand certain concepts. There are lot more statisticians out there who are heaps better than myself and it was excellent experience to read the questions and answers in the discussion forum. Will continue the course and get my certification and enroll for capstone project as well.

By Jérôme S

•

Nov 14, 2016

I really enjoyed the practical part of showing concrete examples and direct applications of Machine Learning algorithms.

Instructors are dynamic and have made efforts to show enthusiasm.

Quiz are too easy though. Completing most of the work would only require an hour or so per week. I hope that the other courses are way more intense and require more work.

Overall, I would recommend this introductory course.

By Oliverio J S J

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May 27, 2018

This course is an excellent introduction to many artificial intelligence concepts, although you do not go into any of the topics in depth. My only complaint is that the material has not been updated to reflect the cancellation of the last two courses of the specialty, so the instructors spend time talking about nonexistent material. It would have been preferable to use free software from the beginning.

By Swapnil S

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Jan 16, 2019

This course is pretty good for someone who wants to learn how to implement machine learning models, how to train data and how to make the most out of machine learning. The only issue i found is they are using GraphLab a lot more where as SciKit learn is the industry standard. Overall, its a good learning way, models and other things you can learn on your own once your basics are clear. Great Course.

By Jose O G

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Dec 12, 2017

The theory provided by the course is really good and I will highly recommend it as an introduction to ML. The practical applications are not as good. They feel rushed and not particularly well explained. You usually get a recipe that you follow through using a tool that hides most of the details behind the theory. You just feel like you would not be able to do the assignments without the tool.

By viyom

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May 30, 2020

Loved the enthusiasm the instructor show while teaching! However I would suggest either to update the course video to consider Turicreate or change the assignment back to Graphlab, as it can be troublesome. Also installing graphlab on windows was a big challenge (at least for me), would really appreciate if there was one video explaining how to do that!! Overall content wise it was nice.

By Charles G

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Dec 30, 2015

Excellent course! My only "complaint" really is that I wish the instructors would use other tools besides GraphLab. I realize that this does make it a lot easier, but I'm sure many of us work in environments where we cannot use GraphLab (for a variety of reasons) and it would be most helpful it they demonstrated how to accomplish the same thing solely using open source alternatives.

By Elizabeth K

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Aug 28, 2018

Love the professors and the curriculum. The video design is quite annoying though -- if I want to rewind to a particular place, oftentimes I would accidentally press the configuration buttons because they're so close to the timeline(?) (I'm not sure what it's called). Also, it would be great if the subtitles didn't have a background color, as it blocks out a lot of the video.

By Roger S

•

Feb 12, 2016

Great introduction to the various topics of the specialization. Introducing each of these topics in a generic way helps to get a sense of what they are about, and what to expect from the specialization. It might be a good idea to be explicit about the fact that this course really only has value added in conjunction with the other courses, and not really as a stand-alone module.

By SaketKr

•

Dec 9, 2018

It was really good.

Pros:

Has really nice assignments.

Teaching is really good.

Cons:

Should've used and open source package. Graphlab is good, I accept, but I wasted like 4-5 hours trying to install it, because some or other errors or dependencies,. I mean some consideration should've been done about an easy smooth method for it, for a beginner like me, it was really frustrating.

By muskan g

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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

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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

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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.