Applications\Data Science\AI\ML - Resources & Links

Data science, AI (artificial intelligence) and ML (machine learning) are considered the new "buzz word" and some people are saying it is "over-hyped". However the truth is that DS\AI\ML was actually "under-hyped" for the last 70 years, since the early days of the development of the computer. ​The critical threshold, however, in terms of public and financial attention, is probably the vast advancement in data collection and sotrage abilities , technologically enabled only in recent years. In fact, it is remarkable to note that more than 95% of all data stored by humanity has been collected since 2012 until today.  We collect here a few links and resources. 

First, you might want to acquaint yourself with one of the following two languages: 
There's some good online resources. Check out for instance: 
If you don't know what "all the hype" is about - the following clips might explain the excitement 
R and Python are the platform to which you import your data - and where you analyse it. They will do what you ask them to - but you need to know what to ask for. Here is where you should learn the algorithms and the ideas behind the algorithms. There are some online resources (see above) - but full details are in books and original research papers. Some good places to start: 

As for research papers, you might want to check the following links:  AI-bibliography

Now that you mastered Python, R and many ML and DL algorithms you might want to put your skills to the test. One of the places to do that is Kaggle. 

​But, where is it all heading? For isntance, recently, one of the most powerful leaders in the world was quoted saying:  
In fact, visionary thinkers have been talking about these subjects for a while now:
In fact these new technological developments make leading indviduals in various industries and sectors speak about the complementing social changes that might result. 
In the meanwhile, you might want to check the following motivating clip: