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

4.6
8,939 ratings
2,140 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

BL

Oct 17, 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

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.

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26 - 50 of 2,062 Reviews for Machine Learning Foundations: A Case Study Approach

By Shibhikkiran D

Apr 13, 2019

This is course is very informative for a beginner. It helps you to get up and running quick provided you have little basics on Python. You should( sideline on your own interest) also pickup Statistics/Math concepts along each module to make a rewarding experience as you progress through this course.

By Diogo J A P

Feb 15, 2016

With a funny and welcoming look and feel, this course introduces machine learning through a hands-on approach, that enables the student to properly understand what ML is all about. Very nicely done!

By Mallikarjuna R V

Jan 17, 2019

Wonderful opportunity to learn and execute hands on coding of Machine Learning. The amazing task that Machine Learning methods and algorithms does behind scene is understood for the following cases / intelligent applications:

1. Regression (e.g. Predicting House Price etc.)

2. Classification (e.g. Product review sentiment, Spam detection, Medical diagnosis etc.)

3. Clustering and Similarity (e.g. Grouping news articles)

4. Recommender (e.g. Amazon personalized product recommendations, Netflix personalized Movie recommendations etc.)

5. Deep Learning and Deep Features (e.g. Google image search, Image-based filtering etc.)

The main challenge for me was to code using “Python3, Pandas and SciKit-Learn” instead of “Python2, GraphLab Create and SFrame”. I am now confident to develop intelligent applications based on Machine Learning. Thanks to Professors (Emily and Carlos) and to Ashok Leyland-HR for giving me this opportunity.

By Yuvraj S

Feb 01, 2019

It is a good course if we take into account the foundational part. But since only one library has been used to solve the issues, one does not explore and write their own functions.

By akashkr1498

Jan 18, 2019

lacture was good but one point i want to share to you don't use rare tools for assignment personally i faced lots of problem while installing graphlab better to switch to some common tools like sklearn python platform .

By Jaime R

Dec 17, 2018

Great introduction course. However, getting the notebooks to work with Graphlab is a real pain. The notebook exercises are also mostly make-work rather than real explorations. The explanations and the notebooks themselves are pretty good though

By Jefferson N

Feb 13, 2019

A good course, but the tools are a bit dated and it's showing its age.

By Dmitry V

Apr 01, 2016

I'm sorry, but this is just ridiculous. I can't recommend this course to anyone. It's all about advertizing: Emily Fox can't stop but recommend Amazon services, and Carlos Guestrin does the same for his Dato's Graphlab Create, which is might be great in general, but absolutely useless in educational purposes. The practice part of every week is just a waste of the time.

I can't say "money well spent".

By Nafi A K

Oct 15, 2017

the course contains misleading information about a capstone project that I discovered -by coincidence - that is no longer exists, the video introduction and the final videos is mentioning the capstone project time and again ! , I think this is a major problem bacause such project was one of the most usefull demonstration of the skills that one could acquire from the course, if I knew this before I would not have enrolled in this course, unfortunatly I discovered this when I am already in the second Regression course!

By Igor K

Jun 18, 2016

I can only infer that this course's target audience is rich pregnant women who care about shoe shopping and celebrities. Unfortunately I am none of those things and had to cringe my way through the examples, watching the videos at 2x speed.

The course itself is incredibly shallow, even for a survey course, and basically serves as an ad for one of the professors' own products -- Graphlab Create. You'll be much better off taking Andrew Ng's course, which is significantly more in depth and forces you to write your own solutions to problems instead of relying on a proprietary library.

The only reason to prefer this course is if you really dislike the idea of using matlab.

By 郑轶松

Dec 27, 2015

LIKE an advertisment!

Why not use pandas and numpy sk-learn?

Open source is more popular!

By Mike C

Jun 14, 2016

The course is basically an advertisement for the software one of the teachers created. I did learn a bit of high level concepts, but when it comes to coding, the answers is always 'conveniently, my software does this for you'. I wanted to actually learn about these concepts deeper, and implement them.

I also was able to complete this 6 week paid course in a few days which should not be possible. I have since started the free Stanford course taught by a co-founder of coursera, and it is MUCH better! I recommend it to anyone!

By Alejandro

Jun 13, 2016

Shame that it was not possible to progress with this course without using graphlab which the creator of this course himself created. Please see the course as just a training sales promotion for his ML application.

By Dmitri K

May 27, 2016

The whole course based on some proprietary software. In general, it seems that the main goal of the course is promote that software.

By john p

May 13, 2016

No Open Source Libraries, this course is not educational; it is a sales pitch to use their expensive software. Good luck having an employer pay this amount of money for software when they can hire employees that can use free open source libraries.

By sravan

Oct 13, 2016

there is no proper documentation.

at least there should be some clear instructions for first program

By Xing W

Jul 03, 2016

I was expecting to solve problems using more open-sourced package. Unfortunately, I feel this series of courses are more of an advertisement for the instructor's software company.

By Charlotte E

Apr 12, 2016

I feel like it should have been mentioned a lot clearer before starting that this was simply a course in how to use the creators library. These skills are not transferable anywhere else as I would have to pay to use them in future! Would have been a lot more useful as a how to for sci-kit and pandas.

By Sarah S

Feb 13, 2016

Unsufficient information for the programming assignments.

By Hugo N M

Feb 07, 2016

The course has a fundamental problem, it relies completely on a library developed by one of the instructors, which is not open source. In the end, it seems like a big opportunity of delivering a marketing campaign by the instructors then otherwise.

I definitely will not spend time and money on the other courses of this specialization.

By Iori N

Jan 26, 2016

i cannot spend $4000 per year package just to learn this course. sorry i am off...

By Yaron K

Jul 13, 2016

The Lecturers are very enthusiastic, but I was hoping for examples and assignments based on Pandas and Skikit-Learn. Instead the course examples and assignments are based on a machine learning package called Graphlab, that stopped working when it was upgraded to version 2 (there are workarounds that enable it to work locally, but clearly it isn't "enterprise ready")

By Mike L

Sep 07, 2016

Might be good for someone looking for a casual overview?

I really wanted to like this course, and was excited about the series. Very disappointing. Refunded after scoring 100% on first three weeks and watching the theory portion of week 4. I was familiar, with the subject prior to taking this course; was hoping for a deep dive.

Too many trivially short and low information density videos. Handwavy mathematics. I would have liked to get a more solid idea of the depth of the series from the first course before committing money.

Default software for the course has near-zero market penetration (per indeed.com), unless maybe you work at Apple -- not really excusable for something that purports real-world value. Yes, you can use other software, *except for the capstone*, per another reviewer: this is fatal.

Presenters just not fully prepared to lecture on the topic: the nail in the coffin was the end of the week 4 lectures on clustering: "So, at this point, you really should be able to go out there and build a really cool retrieval system for doing news article retrieval. Or any other really, really, really cool retrieval that I can't think of right now. But of course there's lots of interesting examples. So go out there and think of ideas that I can't think of right now." Really? How about: "Cut! Take two."

Many poor design choices for the presentation. Too much time spent writing things on slides that should have already been on the slides.

As of this review: no reviews on the last courses in the series, and some poor (but indicative) reviews of the other courses.

By Valentin T

Jan 17, 2016

Using a proprietary library instead of widely used libraries and discouraging the use of open source widely used libraries. It barely compiles, the example notebook has method calls that use non existing methods of the SFrame object.

The course claims that it teaches the student how useful practical knowledge but then ends up using a non standard library and saying not to use pandas or scikit learn.

By Mario L

Nov 24, 2015

I dont like the tools they used, it seems like a promotion for their company.