Artificial intelligence is a broader concept that deals with instilling human intelligence in machines so as to enable them to self-process, self-learn, self-think, and finally self-correct themselves. The key rationale behind the employment of artificial intelligence is to enable machines or systems to act by themselves without the programmer having to explicitly code each and every business logic.

Artificial intelligence

Machine learning, on the other hand, is an application of artificial intelligence – wherein logical algorithms & statistical models are designed & formulated to equip systems to self-learn by identifying data patterns & trends; and drawing inferences & conclusions from the same.

What are the skills required by AI/ML engineers to succeed in their careers?

A successful engineer in the field of artificial intelligence & machine learning needs to have in-depth know-how of data science & statistics; and certain basic knowledge of data processing & software engineering. A few of the important artificial intelligence skills required to be considered by professionals & aspirants in the field of AI/ML are mentioned below for reference –

1. Practical or hands-on experience in data science & statistics

An important factor of consideration is that machine learning or artificial intelligence is not a theoretical or academic concept. Hence, going deep into research & understanding the theoretical nitty-gritty of statistical concepts is not a mandate for becoming a successful AI engineer.

2. Computer programming & fundamentals

In-depth knowledge of computer software fundamentals starting from data structures, trees, graphs, optimization algorithms, linear programming, and computer architecture is required; as the role of an AI engineer would be to simulate a machine to behave like a human. Hence, without understanding the working principle of systems, it would be difficult to cope.

3. Statistics & probability distribution

Statistics form the base of data science; which is an integral part of machine learning. ML engineers must be proficient in probability concepts including conditional probability, Bayesian principles, Markov models, etc. In addition, they should also be aware of univariate as well as multivariate statistical analysis – which would form the base of machine learning techniques.

4. Data modeling & model validation

Data modeling techniques (an offshoot of statistics) are extensively used in machine learning to identify valid patterns & classifications on datasets. Someone who wants a career in the field of AI and Machine Learning needs to be familiar with these skills.

5. Design & software engineering

Although this skill may seem the most irrelevant for a machine learning or artificial intelligence engineer; still professionals or aspirants must be cognizant of the fact that the ultimate product would be software. Hence, basic knowledge of system design & deployment is a mandate for being a successful ML/AI engineer.

Conclusion

AI and Machine Learning are the most in-demand in today's world that technology has taken over. Every company, every business, has use of these two. It is beneficial for you, if you have the aforementioned skills to impress the recruiters!

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