Have you ever wondered why your Facebook feed contains several sponsored advertisements for digital cameras the day after you do a Google search for good digital cameras? Or have you seen supercomputers like Watson max all questions on a show and go on to beat established quiz enthusiasts hands down?
All of it is machine learning in action for you, where those computers or software systems have been fed large amounts of data and also given algorithms that help them think like a human brain would, to make educated decisions. Artificial intelligence and machine learning have captured the imagination of scientists, corporates, users and even job seekers all over the world. As a job seeker, though, you should be clear about the skills you will need before you jump on to the bandwagon.
Also Read: Future of Machine Learning in India
Fundamental Requirements
Many job seekers have a wrong notion that picking up a few programming languages is the first step to a machine learning career. But the skillsets required going some way further back. Since machine learning will involve a non-human entity making correct predictions and inferences, therefore the focus is on cold, hard logic and mathematical principles. You need to have a strong grounding in probability (Markov and Bayes should be names familiar to you!) and also statistics. You need to have the knack for looking at a big pile of data (probably unstructured), and be able to identify gaps in it, and also spot trends and patterns.
Programming Requirements (Skills)
This is the core of the skillsets you would require and would be likely to require you to have a formal certification too. You need to be able to use your programming skills and your knowledge of data structures and computer architecture to create dynamic algorithms. You should be adept at parallel programming, and the basics of stacks and b-trees should come easily to you. A good machine learning engineer needs to be proficient in application programming interfaces (APIs), and also have the ability to use machine learning libraries created by other developers.
Programming Requirements (Languages)
As we know it today, machine learning is technically bound to any one particular programming language, so you do have a choice there. But let us examine the three most popular programming languages that machine learning engineers commonly use. First up is Python whose multiple libraries like NumPy and SciPy (and also specific machine learning libraries like Theano or TensorFlow) make it one of the most popular programming languages for machine learning. Then there is another language called R, which lends itself very well to machine learning.
This programming language is a favorite of data scientists and statistical programmers and has been happily adopted by several machine learning engineers. And finally, we have the old warhorse C++, which is not as advanced as Python or R, but because it is good for networking protocols and infrastructure interfacing, it is still used for machine learning programming.
Apart from these technical skills which you need, two more skills are also necessary – the ability to look at a system holistically (including sales, inventory, billing etc.), and the ability to remember that your output would have machines as the audience, not humans! So are you up for it?
Related Post : What are The Skills You Need to Become a Machine Learning Engineer?
