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Are communication students eligible for machine learning?

Communication students; meaning students of electrical communications and electronics engineering, are an elite stream of engineers who generally have more than average depth in mathematics, signal processing and probability theory. Today, in this video we are going to review the idea that whether communication students are eligible for machine learning even after that.

The good:

Communication students are generally taught 2-3 courses of engineering mathematics and 1-2 courses of compulsory probabilities and numerical approximations. Along with all these mathematics, handling the normal engineering physics courses as well as electronics calculations, they get a good exposure to the mathematical problem setting, problem solving, numerical algorithms and other several necessary tools needed for a machine learning expert.           
What is more important for further advanced experiences in machine learning is understanding of different mathematical objects, such as graphs, networks, series and others and being able to analyze them properly. And this is where being a communications engineer is more able than others. Having courses on signal processing, and grilling their hands on countless numbers of fourier transform breakdown; these things become easy by several degrees for the communication engineers when it comes to understand the different structures associated with machine learning algorithms and learning advanced machine learning algorithms such as time series analysis and others.

The bad:

But a significant part of the current machine learning practices have been the excessive use of different languages as python, r and julia and also expertise in understanding packages or writing codes on a proper level to interact with such tools. This is where a communication engineer lacks the best version. Handling mostly assembly language, micro-controller level or low level language codes like C, they are not best suited for writing fast paced, object oriented and statistical programming focused codes regularly. This is a part where engineers from other streams as computer science do significantly better; which is a worrisome thing in current competitive market.

The best:

Finally to conclude the point, programming is something of a earned skill, and therefore can be achieved over time based on self taught manners. So a communications engineering student who is willing to get into machine learning; has all the proper tools associated to machine learning with them. They know the math, they are capable of performing tough, low-level language programmings. All they need is a refinement, touch of machine learning like thinking and they are good to go.
So if you are communications student; and willing to join the exciting community of machine learning lovers, there have been no better time.
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