A million dollars isn’t cool – you know what’s cool?

//A million dollars isn’t cool – you know what’s cool?

A million dollars isn’t cool – you know what’s cool?


This week we have completed half the syllabus of Data Science, AI, and Machine Learning under Learn with Moolya initiative. I believe it’s time that we look back a little.


A number of participants have asked me this question – How is Data Science, Machine Learning, and Artificial Intelligence connected to each other? 

Most of the way’s AI has been defined – seems to be odd and giving us half picture about how it looks like. Many tutorials, training programs claiming with a blue picture of a Human-robot with two eyes, and write “a machine which exhibited intelligence….” below it and trying to teach us what is AI about. Some tutorials have gone further and taught how to assemble a PC and claiming they are building AI systems by installing few libraries. It’s funny and ridiculous at the same time.

A machine learning system

Below is a simple pictorial representation of how most of the machine learning code looks like. It also shows the percentage of code that is utilized for required operations in analytics process.


Artificial Intelligence

This is how an AI system looks like.

Click Image to Enlarge

Click Image to Enlarge – HD Image http://i.imgur.com/vb6CZO4.jpg


A system of –
1.Well connected machine learning algorithms with each other –
2.Triggering right algorithm at right time
3.Learning simultaneously from its past data
4.Collecting new data and learning & applying it at the same time.



It also makes sense to understand how Big Data architecture comes into the picture. From decades, creating data was by humans. It was a manual process. Now machines are gathering data through sensors, network and human interactions which are creating a lot of data with 4 Vs. A huge data play.  This was also the reason we introduce Big Data concepts in the 4th session of learning program this week. Big data architecture helps in building these systems as it works on ACID/BASE principles.



How can we test these kinds of systems?

That’s a question I asked participants in sessions and got a few insights. This question is one of most cool questions that I came across in recent times and will write about it in next blog post.


Being a Data Scientist doesn’t seem to be cool anymore – what’s cool is a guy a who can test systems built by data scientists in the better way!





By :-
Riyaj Shaikh | Chief Data Scientist | Moolya


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