Higher education in the West is no longer the exclusive club and proxy partner to student debt. This amazing article by NYTimes shows how- the following are shaking the football loving US education giants in their boots while bring balance back to the Force. Big Education for the common people because you need humans to code in the Big Data era.
https://www.coursera.org/ – The pioneer, has the advantages and disadvantages of being the market leader in an innovating. Sure 1.7 million users, but how many are attending how many lectures and how many newly skilled machine learning people are there after 2 courses have been completed by Andrew Ng, and does industry feel the same about the employ-ability of these course takers.
https://www.edx.org/ , and http://www.udacity.com/ – again some course are common across these platforms so more public web analytics would be a welcome step.
But I really liked Stanford’s open source solution, so that anyone especially and including corporations and companies can start creating their own online courses. It is called class2go and is available at https://github.com/Stanford-Online/class2go
Class2Go is Stanford’s internal open-source platform for on-line education. A team of eight built the first version over the summer 2012, and it is still under active development. Class2Go launched this Fall for six on-campus classes and two “massive open online courses” (MOOC’s): Computer Networking and Solar Cells, Fuel Cells, and Batteries.
Class2Go was built to be an open platform for learning and research. Professors have access to the classes’ data to learn how their students learn.
Projects that help class2go.com
Good to see technology and the internet bringing back skills to people globally, so in the future they wont have to use a skill shortage excuse to import cyber technology H1b slaves and coolies from Asia forced to choose between livelihood, family and undeniable economic arbitrage pressure. Maybe they can try and customize it for Africa, or for women or other needy areas as well.
- Keeping MOOCs Open (creativecommons.org)
Revolution Analytics has of course had RevoDeployR, and in a webinar strive to bring it back to center spotlight.
BI is a good lucrative market, and visualization is a strength in R, so it is matter of time before we have more R based BI solutions. I really liked the two slides below for explaining RevoDeployR better to newbies like me (and many others!)
Integrating R into 3rd party and Web applications using RevoDeployR
Please click here to download the PDF.
Here are some additional links that may be of interest to you:
( I still think someone should make a commercial version of Jeroen Oom’s web interfaces and Jeff Horner’s web infrastructure (see below) for making customized Business Intelligence (BI) /Data Visualization solutions , UCLA and Vanderbilt are not exactly Stanford when it comes to deploying great academic solutions in the startup-tech world). I kind of think Google or someone at Revolution should atleast dekko OpenCPU as a credible cloud solution in R.
I still cant figure out whether Revolution Analytics has a cloud computing strategy and Google seems to be working mysteriously as usual in broadening access to the Google Compute Cloud to the rest of R Community.
Open CPU provides a free and open platform for statistical computing in the cloud. It is meant as an open, social analysis environment where people can share and run R functions and objects. For more details, visit the websit: www.opencpu.org
and esp see
Atleast two of the online courses which were offered for free by Stanford, delayed , are now live! yeah!
Even though you’ve already signed up, you need to register and choose a password in the left side of the login box the first time you go to the site.
The class will last for six weeks and officially starts on March 12. We will be covering two topics per week. Each topic will consist of a series of lectures with some embedded questions. There will be a weekly programming assignment plus a weekly problem set. The first week’s assignments are posted and are due on March 26th.
or those of you who want to get started sooner, we’ve already pre-posted the first week of lectures, as well as information about the course requirements, and the first problem set and programming assignment, both of which will be due on Sunday March 25th. You can check it all out now at:
Message from the guys at Palo Alto— Why dont they just make videos using Sal Academy’s help?
We’re sorry to have to tell you that our Machine Learning course will be delayed further. There have naturally been legal and administrative issues to be sorted out in offering Stanford classes freely to the outside world, and it’s just been taking time. We have, however, been able to take advantage of the extra time to debug and improve our course content!
We now expect that the course will start either late in February or early in March. We will let you know as soon as we hear a definite date. We apologize for the lack of communication in recent weeks; we kept hoping we would have a concrete launch date to give you, but that date has kept slipping.
Thanks so much for your patience! We are really sorry for repeatedly making you wait, and for any interference this causes in your schedules. We’re as excited and anxious as you are to get started, and we both look forward to your joining us soon in Machine Learning!
Andrew Ng and the ML Course Staff
I got the incredible and intriguing Machine Learning for Hackers for just $15.99 for an electronic copy from O Reilly Media. (Deal of the Day!)
It has just been launched this month!!
It is an incredible book- and I really like the way O Reilly has made it so easy to download E Books
I am trying to read it while trying to a write a whole lot of other stuff— and it seems easy to read and understand even for non-hackers like me. Esp with Stanford delaying its online machine learning course- this is one handy e-book to have to get you started in ML and data science!!
Click the image to see the real deal.
Message from Stanford –
Dear Ajay Ohri,
We’re very excited for the forthcoming launch of Course Name. We’re sorry not to have gotten in touch lately – we’ve been busy generating lots of content, and the system is working really well. Unfortunately, there are still a few administrative i’s to dot and t’s to cross. We’re still hopeful that we’ll go live very soon – we hope not more than a few weeks late.
But since we don’t have a firm timeline right now, we’d rather leave this open and get back to you with a definitive date soon (rather than just promise you a date that’s far enough in the future that we can feel confident about it). We’ll let you know a firm date as soon as we possibly can.
We realize that some of you will have made plans around expecting the course to start in January, and we apologize for any difficulties that this delay may cause.
The good news is that the course is looking great, and we’re thrilled that over X,000 of you have signed up – we can’t wait for the course to start!
See you soon online!
Course Name Course Staff
Some interesting stats (and note the relative numbers)-
67,000 signups for Technology Entrepreneurship
58,000 signups for Cryptography
44,000 signups for Machine Learning
50,000 signups for Design and Analysis of Algorithms
Check out these other courses:
One of the cornerstones of the technology revolution, Stanford now offers some courses for free via distance learning. One of the more exciting courses is of course- machine learning
About The Course
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Professor Andrew Ng is Director of the Stanford Artificial Intelligence Lab, the main AI research organization at Stanford, with 20 professors and about 150 students/post docs. At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.
- When does the class start?The class will start in January 2012 and will last approximately ten weeks.
- What is the format of the class?The class will consist of lecture videos, which are broken into small chunks, usually between eight and twelve minutes each. Some of these may contain integrated quiz questions. There will also be standalone quizzes that are not part of video lectures, and programming assignments.
- Will the text of the lectures be available?We hope to transcribe the lectures into text to make them more accessible for those not fluent in English. Stay tuned.
- Do I need to watch the lectures live?No. You can watch the lectures at your leisure.
- Can online students ask questions and/or contact the professor?Yes, but not directly There is a Q&A forum in which students rank questions and answers, so that the most important questions and the best answers bubble to the top. Teaching staff will monitor these forums, so that important questions not answered by other students can be addressed.
- Will other Stanford resources be available to online students?No.
- How much programming background is needed for the course?The course includes programming assignments and some programming background will be helpful.
- Do I need to buy a textbook for the course?No.
- How much does it cost to take the course?Nothing: it’s free!
- Will I get university credit for taking this course?No.Interested in learning machine learning-
Well here is the website to enroll http://jan2012.ml-class.org/