AI Skills: Among The Most In-Demand For 2021

Becoming a globally recognized AI professional is not an overnight journey. But the number of people who want to master AI skills just keeps rising. Yes, AI is a very broad field that requires both breadth and depth in understanding its concepts. So, as you begin this journey you need to know that AI is extremely challenging, fun, and fulfilling. A strong foundation will prepare you for more challenging concepts in artificial intelligence (AI). But how can one get started with AI? And what particular skills does it require?

In reality, the year 2021 is the best time to learn AI skills. And there are lots of useful resources out there that will help you to learn those skills faster. In this blog post, we’ll talk about what AI skills are currently in demand and will give you some pointers on where to start learning those skills.

Is AI Technology a Tough Area to Get Into For a Beginner?

A lot of tech companies like Google are refocusing their efforts on AI in order to build a strong AI to solve artificial general intelligence (AGI). Right now, at this very moment, there are hundreds of thousands of AI jobs that are open and waiting to be filled. A career in Artificial Intelligence is surely worth all the sweat and midnight oil you might end up burning.

But what usually stops many students is a vast ocean of information absorbed by learners. And once you dive into it, you’ll maintain a steady pace along with a fair amount of patience to carry on to move ahead in your learning journey. Eventually, your knowledge transforms you into a skilled in-demand AI professional.

Here are a few concepts that are useful to learn to get into the AI field :

  • Multivariable calculus (differentiation, partial derivatives, finding functions’ maxima/minima);
  • Functions (understanding what convex function is, etc.);
  • Matrices and vectors (make your code faster);
  • Syntax of programming languages that you want to use for coding.

AI is so broad that you need to specialize as early as possible. Once you get the overview of the field you need to start going in-depth and know what the modern AI field is composed of.

If you’re a college student who dreams about getting into the AI field, be aware of a basic core skill set you would likely need:

  • Writing non-trivial programs in programming languages is a must (most likely with Python).
  • Basic undergrad engineering math to include calculus, linear algebra, and statistics.
  • Get a solid knowledge of the entire field of AI at the introductory level. Read a book or take the AI-101 online class, get all the basic algorithms for searching, etc. A good book that covers information on AI basics is “Artificial Intelligence: A Modern Approach”.

And, finally, maintain a strong motivation and passion for solving problems and building stuff!

What Does a Beginner Need to Learn to Get Into the AI Field?

An AI field requires some prerequisites in mathematics and computer engineering. To become really skilled at AI basics, you need to master Python since it’s one of the most important languages for AI. Also, a good knowledge of TensorFlow, Java, R, natural language processing can provide you with much better chances of getting hired for AI/ML jobs.

Take a pick at some AI concepts that heavily involve Python:

Here are some stuff that you need to learn in math:

  • Discrete maths is very important for building AI systems;
  • Calculus: both integral and differential;
  • Numerical optimization: convex optimization using both first and second-order optimization methods;
  • Statistics and probability: random variables, probability distribution functions. Bayes theorem and much more such stuff are important. Probability theories help to design systems that work even with noisy or incomplete evidence.
  • Linear algebra: singular value decomposition (SVD).

Useful computer science skills for getting into AI:

  • System design for the ability to put together a working system programmatically given some requirements.
  • Data structures: trees, lists, heaps, arrays, and so on.
  • Algorithm analysis: to analyze the time and space resources consumed by an algorithm.
  • Computational complexity theory: NP-hard and NP-complete problems, etc.

If all this might look complicated at first glance, however, that doesn’t make it really tough. In fact, one can learn AI much faster with all the certified online educational programs available today. There are plenty of online courses available on e-learning platforms that impart AI skills and competencies.

What Practical AI Skills are Currently Most In-Demand?

Here is a list of just a few recent startups/approaches which involve procedures with a large number of AI automated algorithms:

Deep learning. Over the past few years, its development contributed to the advancement of the artificial neural network along with its hidden layers. This approach builds a mechanism that replicates processes taking place in the human brain. This deals with such important senses like hearing and the visual perception of the spectrum of light (human-like vision/hearing abilities). In this case, AI algorithms create computer speech recognition, as well as replicate automated vision abilities.

Rule-based ML. A vast scope of ML methods have been created for identification, learning, or developing more advanced rules. The main characteristic lies within the purpose of identification for further development of relational rules. These rules are represented with data acquired by this system collectively and include learners’ classification and artificial immune systems.

Genetic algorithms. This AI approach has been initially developed between the ‘80s-’90s. Now, this approach helps to improve and further develop more advanced genetic algorithms that deal with mutation and crossover issues. These AI algorithms are utilized for generating a new more improved genotype with much better survival solutions.

Clustering. This is a common technique for statistical data analysis. Different clustering techniques have different make-ups on the structure of data. They are often defined by similarity metrics and are used for recognizing internal compactness between members vs. the same cluster. This also implies different clusters as well.

Learning classification systems (LCS) basically is a set of rule-based algorithms developed specifically for ML technology. They combine innovation components (such as genetic algorithms) along with learning components (supervised/ unsupervised learning and reinforcement). They store accumulated knowledge collectively in order to formulate expectations and to identify a set of applicable context-based rules.

Similarity and metric learning. This area is about the similarity functions (or the distance metric functions) that pertain to new objects.

Artificial neural networks (ANN). This algorithm is commonly called – “neural network” (NN) and inspired by the structure of functional aspects that are pertaining to artificial networks, as well as processing paradigms of neural biology. The computations are constructed in terms of interconnected groups of artificial neurons. Its main purpose is in processing data by utilizing a connectionist approach.

Bayesian networks. A probabilistic graphical model which deals with a set of random variables. And analyzes their conditional dependencies through a directed acyclic graph (DAG). For instance, it represents potential links that reveal connections between diseases and symptoms. Depending on the characteristics, it is able to calculate and predict estimated probabilities of the diseases’ occurrence. There are effective algorithms that do inference and practice. 

What Careers are Available in the AI Field? 

Today, the number of tech companies that are offering jobs in such complex fields like AI, data science just keeps growing. The AI careers include machine learning engineers, business intelligence developers, AI engineering, etc. So, careers in the AI field look and sound pretty promising since the field now is full of opportunities for candidates with the right set of qualifications and experience.

The defined role of AI is still quite an abstract matter. But in day-by-day, its applications are increasing in such important tech solutions as:

  • Face recognition
  • Netflix recommendation
  • Autonomous driving
  • Face mask detection(in recent scenario), etc.

When we talk about career opportunities in AI and related technologies, it becomes obvious that they are quite rewarding. Especially, with almost every industry switching to enhanced services, including automated processes. No doubts, AI is going to expand its domination soon! 

It won’t even matter whether you start your career in designing, software development, or even digital marketing – you will rely on systems empowered by AI and machine learning. So, it is always a rewarding decision to begin your career as an AI developer. Or you can also start getting familiar with concepts of artificial intelligence through online courses that would surely help you in your career endeavors.

In fact, for the best career ahead, it’s better to have a good learning start. So, start pursuing training in AI and move forward with the right training program. If initially, you came from a non-technical background, find a well-structured training program or take online courses where you can pursue the AI basics from scratch. Then master your AI skills and keep abreast with the latest updates in the AI/ML fields. This will help you steer yourself up the corporate ladder at break-neck speeds!


AI is a field that, for sure, requires a maths/logic-oriented mindset. So, paying close attention to maths, like in other sciences, is extremely vital for understanding how the AI world works. Apart from the technical skills like Python, TensorFlow, PyTorch, and other popular libraries like NumPy, Pandas, Matplotlib, Scikit-learn, you must have a strong passion for learning. Also, master the ability to get a good grasp on new things quickly.

As AI algorithms interweave in today’s scenario in many aspects, this makes AI and ML one of the most rapidly growing technologies of the future. No doubt, in the upcoming years industries will require more manpower to have knowledge about ML and AI. And we’ll definitely have more opportunities to learn these technologies more in-depth. So, get the specific domain of knowledge in AI and you will contribute to solving the required business and industries’ problems in the future!

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