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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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2351 - 2375 of 3,115 Reviews for Machine Learning Foundations: A Case Study Approach

By Alec K

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Dec 21, 2015

A great high level overview and introduction to machine learning. The topics broadly cover different machine learning algorithms and their approaches. The only downside is that the proprietary library they use to teach the concepts is incredibly expensive for an individual to license.

By sainik c

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Apr 2, 2016

It easy to start and content is less mathemetical and ready to use knowledge. And tool are helpful to start playing with. May be later go into details . It's perfect for software engineer not having knowledge of ML but wants to start. One can play with data after this course I think.

By Stephen G

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Feb 10, 2017

This was a great specialisation taught by great professors, but I'm very frustrated that the final segment on deep learning was never finished! Coursera needs to fill this gap since it is hugely important topic and isn't covered adequately in any other Coursera material right now.

By Aditi D

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Jul 3, 2020

The course is designed very well, nice way of teaching taking practical examples , it is just that working with pandas wasn't easy,it took a lot of time even to find syntax and graphlab wasn't working tried searching on various forums. Please add ski-learn and pandas syntax too.

By Rui H

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Oct 14, 2017

Pretty good bird view course for entering the world of machine learning. I liked how the course was designed and its contents. It provides us the machine learning ways of thinking and doing, and get us ready to move further and deeper in the path of machine learning. Recommended.

By Francisco D R S

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Sep 21, 2017

Great course to review concepts and learn some new. Using graphlab was great to go through the concepts quickly however it is not the best approach for real life applications. I'm hoping the next courses will go deeper on how to actually work through the models and algorithms.

By Christophe B

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Nov 23, 2016

Very good course. I look forward to starting the next ones, in the context of the ML Specialization.

One small caveat: I would have preferred using a more widespread and open source ML library, than GraphLab Create, which I am not certain to encounter again in the "real" World.

By Ali A

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Feb 14, 2017

A great intro course to machine learning concepts, the only problem with me is the environment, its course dependent and don't feel like it can be widely applied in various fields

i would love if tensorflow or sikitlearn was adopted through the course

Great course though !

By Mika

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Apr 5, 2016

Great course! I think much of it will make a lot more sense in hindsight after I've gone through some of the other courses in the specialization. The main reason I'm not giving it a full five stars are the many mistakes and ambiguity, especially in some of the assignments.

By Bernardo F M

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May 30, 2022

A parte teórica do curso é muito bacana e aplicada, porém a biblioteca utilizada ao longo do curso não é a mais indicada, dado que muito do que foi passado caiu em desuso e também já existem bibliotecas mais populares para desenvolvimento de sistemas de machine learning.

By SHUBHAM A

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May 29, 2020

I really liked this course because I was able to actually implement different aspects of Machine Learning which helped me understand the underlying concepts better. I wish that the algorithms were taught with a little more specifics, but overall it was a nice experience.

By Raymond A

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Jun 3, 2017

Top down, concept to underlying details learning approach is welcome, as is the informal communications style and traceability of instruction to quizzes. Only negative is some confusion regarding what was initially versus currently planned for the entire specialization.

By Dinesh L

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Sep 18, 2016

Very nice designed course!

Only flaw is its stuck with a paid license of Graphlab and doesnt focus on free source so that algorithm can be used with the help of free modules in future.

It would have been better if the examples were done using free tools, like scikit learn

By Conrad T

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Jun 7, 2016

Machine Learning Foundations was an excellent overview of the many areas within machine learning and how these techniques can be applied to solving real world problems. Now, I look forward to continuing with the remaining courses of the Machine Learning Specialization.

By lionel b s p

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Feb 22, 2020

The course was great but the material is not up to date and the support documents are not aligned with the video. The last course about the deep learning was quite messy and the programming assignment was not clear with many many repetitions. That one was not good.

By Marc P

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May 15, 2017

This course is very useful to have a practical overview of the machine learning algorithms and techniques with out diving in complex topics (at least at the beginning).

It's also useful to learn a high level library to manage machine learning algorithms and concepts

By lalith

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Mar 29, 2020

The concepts on recommendation systems could have been explain better. Having to use TuriCreate instead of open source libraries is a major drawback. Turicreate is also so high level its hard to understand whats happening under the hood. Everything else is great.

By Dávid H

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Mar 13, 2016

Most of the course was fantastically composed but 15% of it felt a little bit sloppy. I was lucky to be not the pioneer of the course because of the discussions which helped me out quickly in these rare occasions. I would give 4.5 starts if it would be possible.

By Shikhar V

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Feb 26, 2016

Great specialization series! The way of covering concepts is very interesting. I find it somewhat difficult to solve the assignment using Pandas and scikit-learn. The assignments should be provided with enough help for solving them using these open source tools.

By Halimat A

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May 2, 2016

Really good theoretical introduction but couldn't do some of the tasks because my python skills weren't up to scratch, however the course says you don't need any previous python knowledge, so I disagree with that. However, overall a really good introduction.

By Zikun T

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Jul 3, 2021

Generally speaking, this introduction-level course is great, which gives machine learning beginners some intuition in this field. But it is not enough for finding a job or doing research, I think finishers of this course still need to learn more to advance.

By Roberto B G J

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Feb 26, 2018

Very good practical approach for an introductory course, however you should have some basic knowledge of programming otherwise it'll take you too much effort and time to complete the tasks assigned. It is also great that Python is being used in the tasks.

By Ritwik S

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Aug 2, 2020

The videos are a bit old still the course is very helpful and really enjoyable. The Case Study approach is something that really help me learn things and keep my interest engaged. Thank You prof. Carlos and prof. Emily for making this course a great one.

By Fernando M

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Mar 12, 2020

It was a good introduction. Although, I would say that, being part of a specialization, I would prefer a more brief introduction, because very important points are left for the future and you have to spend too much time in a content tha is incomplete.

By Justin T

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Apr 10, 2016

The last module was a bit hard for me to understand, but everything else was presented in a very clear way. Great class overall and highly recommended for those with some basic Python skills and a desire to see what machine learning is capable of.