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

4.6
stars
10,497 ratings
2,527 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.

PM

Aug 19, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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2301 - 2325 of 2,449 Reviews for Machine Learning Foundations: A Case Study Approach

By Sander v d O

Apr 01, 2016

This course is for you if you really don't know anything about Machine Learning and nothing about Python. If you do know something about it, look for a different course.

I learned the most from lesson 5 and 6 about recommenders and deep learning because I knew nothing about these subjects.

The programming exercises are disappointing: just cut and paste. I found this demotivating.

By Sean I

Nov 05, 2017

I wish they used open source tools for this. I will not be paying for a GraphLab account nor do I see myself using it in the future. I felt less inclined to strain over learning the API and was unused by the technologies. Other than that the course is pretty interesting as I was able to apply some cool data analysis using ML practices I've learned in other Coursera courses.

By James H

Jul 01, 2020

The course was good, and the instructors did a good job. There don't seem to be any mentors in the forums who are helping, and the library used for the exercises was changed from the one in the lectures. The specialization seems to have been abandoned before they published courses 5 and 6, so ignore every time they talk about how great the capstone project is going to be!!

By 熊华东

Jun 28, 2018

Lectures are great. Unfortunately, i can' t install graphlab create on my windows 10 labtap.I wasted two whole day on it!!!!! I tried every methods google told me, all fail or with bugs. I think pandas and sklearn are far more user friendly.不建议大陆使用windows的朋友尝试安装graphlab create,标准安装方式即使用了VPS也网络链接失败,用anaconda安装的话,anaconda3可以安装,但是没有canvas功能,anaconda2各种奇怪报错。搞了两天失败,我还是用sklearn。

By S M R A

May 09, 2020

This course needs to be updated. Windows don't support TuriCreate or Graphlab. Because it works on python 2. But now python 3.8 has come and TuriCreate doesn't work in it either. So, I had to use Ubuntu in my virtual box to work on the assignments. The course wasn't bad. But if they update the course, it will be a great one for beginners in machine learning.

By Katya H

Apr 26, 2016

I think it was a good introductory course. However, I think it was too simple: assignments required no more than copy+paste from the lectures.

I understand the primary goal is to hook people up on how good graphlabs is, but I'd rather leaarn numpy, sklearn and other widely available tools. At least show both in the leactures. Please :)

By Iker U

Apr 11, 2017

This course presents an overview of different machine learning tools but I rather prefer starting from the second course were more specific competencies are given. I believe that in courses like this the contents are to sparse.

It would serve as an introduction. But the contents of week 4 and 5 are not even in the specialization!

By Bryan D

Sep 25, 2019

The course teaches a a lot of information and explains everything from a beginners POV which is great. I only have 2 issues with this course, the use of proprietary software instead of all open source software and NO CONTINUED SUPPORT for about 3 years since the course has been out. Either update the specialization or cancel it.

By Salvador V M

Nov 04, 2017

Good for start in machine learning concepts. Good because they use Jupyter Notebook an python (they use python 2, it would be better 3). But I don't like much the graphlab library for data frames. And also the quizzes are a bit difficult. You have no the whole information in the documentation to solve them.

By Romain R

Apr 10, 2017

The content is really good, well explained=> 5 stars, nothing to add.

Why the 3 stars then ? Graphlab. If you use the pydata stack, as it is said to be possible in every assignments, you get stuck on the quizz due to variations on data and the algorithm used, so you can't really get quite the same answers.

By Vivian Y Q

Jun 01, 2018

Videos were too short to go into details. Too much reliance on the package they development themselves, though I appreciated the simplicity, I don't get to learn about a lot of technical details. So you know how to run a image retrieval model without knowing what are the deep features, for example.

By Troy D

Feb 05, 2020

Good course, learned a lot of basics. I think this course is rather old though and getting a lot of the required software up and running required a lot of work since there are much newer versions available now. I found that I had to do a little extra to get the older packages working in Jupiter.

By Aleksandar S

May 25, 2016

The course content is great. It gives overview on what is going to be learned in details in the next courses. Considering that it is an introductory course and the fact that it utilizes the GraphLab library as tool, I believe it is overpriced compared to the other courses of the specialization.

By Yaniv S

Jan 15, 2017

The whole eco-system is based on Graphlab create which is not very commonly used in the industry. The "Programming assignments" are very much like the exercise done in the videos - so no real thought and effort were needed. The Deep learning part is really bad thought and bad examined.

By Eric J

Jul 12, 2016

The enthusiasm of the instructors was the best thing about this class. But I really wanted a more rigorous methodology - and didn't really get it here. But it was an alright introduction to machine learning but not enough if you want to know what makes the 'black box' work.

By Paulo S B d O F

Sep 05, 2016

Pros:

(1) Teachers know what they are talking.

(2) They are energetic and funny.

Cons:

(1) The course uses proprietary and expensive tool.

(2) The course is too simplistic.

(3) The teachers, although they know what they are talking about, they aren't very good at teaching.

By David K

Mar 01, 2016

I think that the course is redundant, it is to general, trying to capture to much, and using a commercial program tool that's doing to much behind the scene.

The second course in the specialization is really great though and you wont miss anything if you skip ahead

By Varun J B

Sep 24, 2015

A lot of problems with software installations. But, the professors for this class seem to be very passionate about the course and they teach well. If not for a lot of problems faced during software installations(which is still not resolved), would have given 5 stars

By Michael C

Apr 10, 2016

Really just an overview of the topics to be explained in detail afterwards.

Big plus for the use of python + notebooks but otherwise, if one is interested just in the overview and not in all the specialization, maybe the Andrew NG course is more detailed.

By Bernardo G C

Jun 08, 2016

El curso tiene mucho potencial, pero hay que afinarlo.

Pienso que los vídeos deben ser reeditados. Tienen errores y conceptos confusos. Deberían ser tan claros como para lograr tomar buenos apuntes y usarlos en las tareas. Las tareas son casi mecánicas.

By Rishi H

Jun 11, 2019

Content and material is good and the trainers are good. Only issue i found is course assignments are heavily dependent on Sframes and graphlab which does not work most of the times.,they should go with panda libraries which is easily accessible.

By Aman S

Jun 14, 2018

The worst thing about this course is graphlab. Trying to run it since last 10 days with the help of every available online resources, but in vain. There are many flaws in graphlab. I tried a hundred times to view images in graphlab, but in vain.

By Juarez B

Jan 12, 2017

This course introduces the key topics of Machine Learning, but the math behind the algorithms is not explained and the programming exercises are too easy. Unfortunately, it also relies heavily on graphlab instead of using open source software.

By Mohit S S

Aug 07, 2018

Course contet is ok. But, intructors really need to teach in a platform neutral way or some other popular library for which ample support is available. In my opinion, learning a tool which is nowhere used in te industry is not a good idea.

By Tarek M s

Nov 05, 2017

the course is good for starter but according to its repetition I waited more .

one star down for many useless information in lectures about Amazon products and so on.

one star down for forcing using unpopular python library .