Machine Learning

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Below are the top discussions from Reddit that mention this online Coursera specialization from University of Washington.

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning.

Data Clustering Algorithms Machine Learning Classification Algorithms Decision Tree Python Programming Machine Learning Concepts Deep Learning Linear Regression Ridge Regression Lasso (Statistics) Regression Analysis Logistic Regression

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Taught by
Emily Fox
Amazon Professor of Machine Learning
and 1 more instructor

Offered by
University of Washington

This specialization includes these 4 courses.

Reddit Posts and Comments

1 posts • 88 mentions • top 26 shown below

r/MachineLearning • post
52 points • fsqcds
Machine Learning Specialization -
r/datascience • post
8 points • mrgann
Recommender Systems and Capstone courses removed from UW Machine Learning specialization without warning on Coursera

The last two courses of the UW Machine Learning Specialization have been removed (after being delayed for several months). People subscribed to the course have received the following email:


> We’re writing to tell you that due to unforeseen circumstances, courses 5 and 6 - Recommender Systems & Dimensionality Reduction and An Intelligent Application with Deep Learning - will not be launching as part of the Machine Learning Specialization. We understand this may come as very disappointing news and we’re deeply sorry for this inconvenience. Please know that if you have paid for these courses or have received financial aid from Coursera, you will remain eligible to earn your Specialization Certificate upon successfully completing courses 1- 4 of the Specialization. If you paid for courses 5 & 6 via a pre-payment toward the Specialization, Coursera will be providing you with free access to two other courses offered by the University of Washington: Computational Neuroscience and Data Manipulation at Scale: Systems and Algorithms. More information regarding this opportunity will be shared in the upcoming weeks.

> If you have any questions or would like to request a refund, please feel free to contact Coursera’s 24/7 learner support team via the Request a Refund article in the Learner Help Center. The last day to request a refund will be April 30, 2017. We value you as a Coursera learner and want to ensure that your experience with the Machine Learning Specialization remains a positive one.

> Regards,

> The Coursera Team

r/MachineLearning • post
5 points • Inori
Has anyone done Machine Learning Specialization on Coursera?

Talking about this one.

What are your thoughts? Specifically, I'm interested in the capstone project since it's the only one I can't preview without paying.

I'm currently going through the Regression & Classification courses and they seem good enough that I'm considering buying the whole thing. Learned quite a few things, despite completing the course by Andrew Ng.

r/argentina • comment
4 points • RandomGuyFromAR

Excelente! No dejes de postear los resultados :) Yo estoy haciendo otro curso de ML (este ) y después arrancaré con alguno de deep learning. Creo que una de las cosas que más me cuesta es encontrar ejemplos para aplicar jaja

r/cscareerquestions • post
4 points • TrueJediPimp
Machine Learning Certification job prospects?

Hey guys, I've been looking into this Machine Learning program online on Coursera, and I'm wondering if this type of certification translates to job offers in Machine learning well? Anybody have experience with this program? I have a degree and multiple years experience and wondering if this would open some other doors as its always good to be diversified in skill sets. Thoughts?

r/computerscience • comment
3 points • reddstudent

UW has one too:

r/deeplearning • post
6 points • theironhide
Which course to use for a dive into deep learning after Coursera Andrew Ng's ML?

Hi, I have a sound knowledge of image processing (mostly low level image processing, object detection, basic object recognition), and I have done undergraduate courses in image processing (equivalent to the Gonsalez Woods book) and machine learning (and the Andrew Ng Coursera course). I want to begin with deep learning with focus on image processing and recognition. The way I see it, the ideal place for me to go would be Fei-Fei Li's course CS231N at Stanford. But before that, would it be better if I took one or more of the following courses?

  1. University of Washington's Machine Learning Coursera specialization - set of 5 courses (

  2. Geoffrey Hinton's Coursera course on neural networks (

  3. Hugo Larochelle's class on neural networks (

Which of these three should ideally be taken in order to strengthen concepts and are worth taking? Which order should these courses be taken in?

Thank you.

r/MachineLearning • comment
1 points • brjh1990

^ That's the one. I also recommend this one..

Like Andrew Ng's course, it's fairly comprehensive in theory and application, though the libraries they use aren't industry standard (it's actually created by one of the instructors), but I found it to be indispensable.

I'd also check out some promising courses on Udemy, as they're nearly constantly promoting 'two day only' specials, though I haven't made it far enough to tell you how comprehensive they are in terms of theory or application.

r/datascience • comment
1 points • thoo17

I really like this series from coursera. These can give you a very solid foundation. For neural network, I will vote Standord courses. I wouldn't worry about pandas,sciket-learn at at this points. When you work on the projects, you will get familiar with these libraries.

r/artificial • comment
1 points • CluelessGoals

> machine learning course and specialty

Hey, sorry is this the one you are referring to? I tried looking for it but this is the closest one i've found. It is created by University of Washington:

edit: nevermind! I found it

r/OMSCS • comment
1 points • chickenPadTai

There is a whole Coursera specialization that is 4 courses on machine learning from University of Washington that uses jupyter/python notebooks to teach you.

Here's the link:

r/IOPsychology • comment
1 points • nckmiz

Yeah same. We had methods courses, where linear regression was talked about conceptually, but we didn't get into the math at all. It wasn't until I took this specialization that I realized there was both a closed form and gradient descent method of calculating the loss for linear regression. Apparently my suggesting we should have stronger quantitative skills for the real world was considered uncouth.

I did learn it all on my own and honestly probably has helped me far more in my career than 75% of the seminars we were offered, but my point was it seems fairly common to get pretty mediocre quant training in I/O programs aside from a select few. Ones that come to my mind are UIUC, and BGSU, and I'm sure there are more, I'm less aware of.

r/datascience • comment
2 points • My-Revised-Identity

I highly suggest this one:

University of Washington Machine Learning Specialization

it's more machine learning focused rather than the Data Science with R course that I think you're talking about; which I find to be more Data Analytics.

However for analytics like you find in that course I don't suggest Coursera, I think Kaggle's tutorials do a really good job of transferring what you'd learn in the R course to Python. I would suggest looking over the Kaggle course and then before you get into Kaggle as a platform, doing the courses I recommended above.

r/learnprogramming • comment
3 points • mkhdfs

This is a famous course, and is the course that put coursera on the map. It tells you everything you need to know about machine learning from the basics to the very beginning of the advanced. You'll learn the core concepts of what machine learning is, what kinds of problems it can solve, and how to apply it given your problem. You'll learn about both regression and classification, supervised and unsupervised learning, and several prominent ML algorithms, as well as vital concepts like cost functions, gradient descent, and broader concepts useful for implementation such as vectorizing for data representations. I can't praise this course enough, it gives you all the tools you need to prepare for more advanced learning and practice in all things ML. You can come from virtually nothing too, all that's required is around a high school senior's level of math. Arguably though a background in linear algebra is required for the course. Linear algebra is central to ML, and personally I was lucky enough to have taken an extensive intro course in linalg back in college, so I knew the concepts of matrix multiplication and transposition. But even if you haven't, Andrew Ng provides an optional set of videos at the end of the first week of this course, and goes through all the concepts of linear algebra that you'll need for machine learning.

As for Neural Networks, this intro course only covers very basic NNs for a couple weeks in the middle of it. In the NN assignments in this course, a pre-built model is given to you with only one hidden layer in the net. The goal is to basically define all the auxiliary functions required for the NN to process its node and one layer, and how to basically navigate through a layer from input to output. So unfortunately the neural network section of this course isn't very robust, but I highly recommend knowing this first before moving onto Ng's neural network courses that ARE robust and show fully how to forward and backprop through many hidden layers instead of just one.

Every other algorithm, including the most popular among ML practitioners like linear regression, logistic regression, k-means clustering, and support vector machines are all brilliantly covered just in this course and you'll get sufficient knowledge in all of them. For the time being, these algorithms are still dominant in real-world ML. However this is likely to change within the next decade or so, since neural nets and deep learning are experiencing a huge resurgence after being basically relegated to university researchers for so long.

So take that one course I linked, and at the end, see if you like machine learning, and decide where you want to go next in your "machine learning learning". If it's neural networks you're after, go to Andrew Ng's Deep Learning Specialization. If it's the more currently-practiced standard algorithms you want for immediate industry applications, try the University of Washington machine learning specialization. I myself am going through the deep learning spec by Ng (on course 4 of 5 currently), but am also very interested in the UW spec, and plan to take that once I have enough spare time to do so.

I hope that helps, let me know if you need anything else. Oh, and do check out /r/machinelearning as well as /r/MLQuestions where people will help you on some of the more basic concepts (and be patient with you while doing it).

r/cscareerquestions • comment
5 points • gerradisgod

I'll recommend the one that everybody recommends. Andrew Ng's Coursera class, Machine Learning:

Since you say you're strong in math and physics, I'll recommend some math heavy MOOC's as well. As a side note, physics is a VERY good major for any tech position btw. Criminally underrated as a second major on this sub.

There is a good one from Stanford like Statistical Learning on Stanford Lagunita:

And also CalTech's "Learning From Data":

A less mathy one is UWashington's ML Specialization on Coursera:

A good resource for learning ML using Scikit-Learn is Udacity's Intro to Machine Learning:

That should keep you busy for a while :)

r/OMSCS • comment
1 points • aProspectiveStudent

I suggest finishing your current master's and supplementing it with ML MOOCs. These specializations, for instance, are excellent:

r/learnpython • comment
1 points • mul8rsoftware

here is the course offered on coursera by Stanford and is free of cost Both of udemy courses are also good you can have a look into this as a second option

r/SubSimulatorGPT2 • comment
1 points • machinelearningGPT2

If you are interested in machine learning implementation you can watch the lectures here:

r/learnmachinelearning • comment
1 points • salimmlkti

I feel by platform you mean some kind of tool. I wouldn't suggest that. Machine learning is more of a science than toolset. It is not like a programming language. Once you want to do something new or improve your results, knowing the math behind it really helps. I would suggest investing on the algorithms and the math.

My suggestion is to take the Machine Learning course by Andrew Ng in Start there and take more classes from coursera. If you wanted to continue with something more project oriented and longer I would recommend from University of Washington. I haven't taken it but Carlos Guestrin is really knowledgeable.

At that point you'd find yourself open to getting your hands dirty on any tool out there.

r/learnprogramming • comment
4 points • my_password_is______

all of

course 3 in

course 8 in

all of

various courses by Andrew Ng (Co-founder, Coursera; Adjunct Professor, Stanford University)

any of

r/learnmachinelearning • comment
3 points • prayagupd

I found "ML with TensorFlow on GCP" in coursera. You can see all the ML courses available in coursera here - I had previously attempted to learn "ML Specialisation" course by University of Washington ( a year ago but could not continue because of my work.

I also had attempted to learn ML with a book "ML in action" - but honestly not an exciting book at all.

I actually learned a lot from Google course. Many things did not make sense in the beginning but I kept continuing. I liked their Quizzes/ Multiple Choice Questions. Course also provides notebooks for exercises (which you practice on Google datalab -

I would also recommend "hands on ML" book. At least it was helpful to me reading the stuffs in a book that Google course taught in a videos.

"ML on GCP" course structure is;

1.How Google does Machine Learning - talks about basic ML stuff that you need data first for ML

2.Launching into Machine Learning - talks about Supervised Learning - Linear Regression -> Perceptron -> Neural Net -> Decision tree -> Kernel methods/ SVMs -> Random Forests - optimizing loss function (using GD) - Generalization and Sampling

3.Intro to TensorFlow - talks about what is tensor, flow (graphs), tensorflow, Lazy Evaluation

4.Feature Engineering (this is the most important course) - talks about how to figure out features (or data) required to predict the label (target)

5.Art and Science of Machine Learning - I still have to complete this course

There is also a free "crash-course on ML" from Google which I have not taken -

r/OMSCS • comment
1 points • Djsn2WSbSaynsEelaEO5

On my Georgia Tech (GT) application, I had some undergraduate courses from a minor in Software Engineering (Matlab, C, Java, networks, etc.) at a state university, some work experience (emphasized appropriately in the application), the Udacity Nanodegree, 1 Coursera course, and (nearly) 2 Coursera specializations.

When I read people recommending taking accredited courses for credit and solely for admission, I am a bit skeptical that is the most cost effective way, but I don't have the knowledge or authority to definitively say what is best. Online offerings these days are really great, and I will say that I feel I learned more from finding challenges in online courses and sticking with them to completion compared to the undergrad classes that I took. However, GT might value an accredited course more, so hopefully someone else can shed more light on the subject of accredited courses than I have done.

Udacity's pricing and content were great and motivated me to study every day by the time I was working on the capstone. They also have a career department which I have access to even now several months after getting the Machine Learning Nanodegree. The challenge of the course increased to the capstone which was nice, and it was a great introduction to machine learning in Python. One consideration is that in the ML nanodegree a lot of the surrounding code was already provided, so it is a focused challenge to understand and complete the machine learning parts and was not for general computer science skills. You might look at their other nanodegrees if you are looking for a more general computer science problems. I am quite happy with the nanodegree I finished and the career planning benefits, e.g. resume review, that goes with it.

Coursera has some very attractive offerings and the pricing is quite good for many of the specializations. I saw within the r/omscs FAQ (I think) a recommendation to take the Data Structures and Algorithms Specialization (DS&A) and I am very pleased with the challenge and content. I was able to finish the minimal set of problems within about a month (finishing 5/6 courses in time for the GT application deadline), then went back and have completed all but the final two problems as of now, and when looking at the GT Graduate Algorithms (GA) content, I definitely feel prepared for it. I even used a GT GA video on Udacity to help me with one of the problems in Coursera.

[Also on Coursera, I finished the Machine Learning Specialization from UW and Machine Learning Course by Stanford. They were easy for me after the Nanodegree, still worthwhile, though the Stanford Course was a single payment which ended up costing more than if they offered a subscription because I finished in a week, so for my schedule I try to stick to specializations with a monthly recurring charge.]

Coursera's catalog is a bit difficult to surf effectively, but they have some very attractive specializations in and out of computer science.

My advice (given with a recommendation of calculated skepticism) is to:

  1. pivot in your work to computer science tasks if possible
  2. complete projects outside of work that demand computer science skills
  3. finish some appropriate and challenging online courses (and buy the certificate for credibility); if you only do one I say DS&A
  4. apply to GT as many times as you can starting immediately, because I think you will get feedback from applying on what to work on

After a year of concentrated effort I expect you should be likely to get in by your target date and have a much stronger resume. Also get someone to proofread your written application materials (multiple times if possible) and get some authoritative recommendations.

Good luck!

r/datascience • post
2 points • LearnDataSci
Coursera's Specialization Opinions

Coursera has a ton of specializations within the data science realm. There's Data Science, Genomic Data Science, Big Data, Data Mining, Machine Learning, Data Analysis, and a few more.

What are your opinions about these tracks? If you have taken multiple, how do they compare? If you are currently in one, how do you think it's going? Do you have ideas on how they could be better?

r/datascience • comment
1 points • RexLaurus

Hello everyone,

I'm trying to get into a data science from from a Electrical Engineering background.

I recently graduated and have experience coding in matlab and python (and others). I've completed a couple of the 365datascience courses that were free for a while as well as some of the kaggle micro courses.

I'm trying to find something a little more challenging and have been looking at Coursera specializations for way too long now, seems like everyone of those has one major drawback and I'm having a lot of trouble deciding between them. In short:

- John hopkins : Uses OCTAVE and from what I understand Python is the way to go.

- Standford (Andrew NG) : Said to be very theoretical, doesn't prepare you to make your own projects.

- U Michigan: Said to be outdated, problems with exercises scoring, bad reviews all around.

(This information is based of reading reviews and reddit posts so I may well be wrong. )

I've also found University of Washington's and IBM's that seem to have good reviews but I'm not sure nonetheless.

Can anyone point me towards the best one of these (or others) considering I have previous coding experience and know some of the very (very) basics of Python DS libraries?

r/learnprogramming • comment
1 points • jonnypajama

I am close to you in age and I had pretty much forgotten programming in the last 10 years or so - I started relearning and I feel much more confident in my skills

My one tip would be to find good teachers - anything that is worth learning is usually paid - my fave one is this:

I've taken quite a few of their workshops and you can even attend online or in person


I haven't taken this course but it's highly recommended from friends:

For AI, I took 1/4 courses here - excellent mix of theory and practice:


r/learnmachinelearning • comment
3 points • anon35202

If you're ready to make a commitment rather than just purchasing a 25 pound book that's going to gather dust on your book case. And you're sure you want to go for machine learning rather than just farting around and fucking around with bits and bobs, then pay some money for people to train you, there are hundreds of courses out there.

The big brick of a book is just going to burn you out because when you get to page 15 you won't feel like you're getting traction on the material. Getting into machine learning is like getting in peak physical shape, you need a personal trainer to yell at you when you're not getting up early in the morning, setting big goals and then meeting them.

Browse through these links, and find a paid course, one where you pay something like $300 to $1000 from a respected school, and take just once class. Make sure it's graded, with lectures, projects, assignments and a final exam. If you do this, you'll learn to use the material in a real setting rather than just exposing yourself to it.

Andrew NG's coursera course on machine learning:

Stanford's Andrej Karpathy course cs231n Computer Vision with Convolutional neural nets: Github: Youtube link to lectures:

Many different courses under Udacity's nanodegree: Like: Tucker Balch's course on machine learning:

MIT Open Course Ware Machine Learning:

FreeCodeCamp and Datacamp has some really good content (focus on Python, stay away from the landfill fire that is R): Like for example:

Stanford Machine Learning:

If it's that exciting and you're ready to commit to it, then consider going for post secondary education. A college degree in Computer Science with focus on machine learning, then a masters degree in computer science with a focus on machine learning. It'll set you back a lot of money, but it's an investment in time and money, and in theory should return ten times as much.