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

4.6
stars
9,355 ratings
2,244 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

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.

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

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26 - 50 of 2,165 Reviews for Machine Learning Foundations: A Case Study Approach

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 郑轶松

Dec 27, 2015

LIKE an advertisment!

Why not use pandas and numpy sk-learn?

Open source is more popular!

By Arun J

Sep 18, 2018

not useful since the material covered lacks any rigor.

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 Shane C

Nov 16, 2016

I did not find the course very good. I came into the class with only real basic knowledge of Python and I was hoping to be able to pick up more as part of this class. WHile I might have picked up more, it was only because I used resources outside the course.

The video instructions in programming in Python left quite a few gaps to figure out by reading documentation. The videos themselves were divided into two sections -- first a theoretical or classroom like section and a second a lab/programming section going over some coding in Python. The 'classroom' type lectures were pretty reasonably good. But the lab/programming were pretty terrible.

They instructor really break down the syntax of the code and just left the student to figure it out. This made being able to take this code and to adapt it to other uses very difficult.

I would not recommend this as a course to help learn Python.

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 Jonathan W

May 31, 2019

The course includes some good, basic, information on machine learning. The instructors seem to know the material well. However, the exercises and coding are based on a python package written by one of the authors that, while free to students, does not easily translate into common packages such as Numpy, Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, and Pandas. Also, the package used only works in Python 2 (which will no longer be supported as of January 2020).

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 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 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 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 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 Christopher W

Oct 15, 2015

The fact that the class uses GraphLab instead of pandas/numpy/sklearn should have been stated up front

The course felt like an advertisement for the professor's toolkit

It was very disappointing that the equivalent standard workflow was not supported

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 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 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 sravan

Oct 13, 2016

there is no proper documentation.

at least there should be some clear instructions for first program

By Iori N

Jan 26, 2016

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

By Mario L

Nov 24, 2015

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

By Sarah S

Feb 13, 2016

Unsufficient information for the programming assignments.

By Ken C

Feb 04, 2017

Not happy about course 5 & 6 got cancelled.

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 Muhammad W K

Jul 21, 2019

A great course, really designed to understand the underlying core concepts of machine learning using real-life examples which takes you through all that with little to no programming skills required!

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 Pooja M

Aug 19, 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.