Vital Skills that Every Candidate Should have to Land a Job in The AI Industry

Numerous technological advancement has taken place over the past decade, more since the pandemic in 2020 in the field of Artificial Intelligence (AI) and Machine Learning (ML). AI has impacted our lives immensely and has become part of our day-to-day lives. Businesses have automated almost all of their processes and operations. AI technology has led to self-driving cars and helped the healthcare sector detect diseases in patients with cutting-edge technology. The need for AI and ML engineers has also grown with the growth of this industry. The demand for candidates with the knowledge and skills required is increasing rapidly. Companies lookout for certain important skills in candidates while recruiting in the Artificial Intelligence Industry. These top skills can be divided into two parts, technical skills, and non-technical skills.

Technical Skills

Technical skills refer to the expertise in certain specialized knowledge demanded to achieve complex actions, processes, and tasks associated with computational and physical technology. Some of the important skills in the AI industry are:

1. Mathematics for data science

Mathematics is vital in AI and the machine learning realm. Concepts like calculus, linear algebra, probability, and statistics are integral in the AI world. Knowledge in concepts involving derivatives and integrals is recommended to learners. Additionally, matrices and matrix multiplication are imperative. Probability theory used in algorithms like Gaussian Mixture Models, Hidden Markov Models, and Naive Bayes is vital to flourishing in this industry. Furthermore, statistical theories like standard deviation and mean are necessary to perform technical analysis of data.

2. Programming Language

Programming languages like our human languages are artificial languages used to control and monitor the actions and behavior of machines like a computer system. It is the heart and soul of Machine Learning as it is one of the most prevalent languages. It is recommended to have a solid command in different programming languages like Python, C, Java, R. Python is essential in machine learning. A hands-on experience in this field is an obligation. Someone who wants to thrive in this industry would be better off when they don’t limit themselves to just knowing Python but get acquainted with the knowledge of other popular languages in AI and machine learning so that they stand out from the rest.

3. Neural Network Architecture

Neural networks are designed to mimic the function and performance of the human brain. As neurons are information messengers in the human body, the neuron in Neural Network mimics the performance of a biological neuron. It is a practical part of deep learning, a branch of AI to solve complicated data-driven difficulties. Neural networks have been used in various domains like the healthcare sector and business fields. Neural network architecture can be seen in technologies like face recognition and speech recognition beyond human capability to code.

4. Signal Processing Techniques

Signal Processing Techniques are a crucial feature of machine learning. The practice of using signals to analyze, modify and manipulate signals is known as signal processing. The knowledge of such skills is extremely useful. It helps in separating data from noise like identification of airplanes by radar, compression of images, conversion of text to speech, and vice versa. Other areas where this expertise is necessary are fields like voice recognition and speech processing. Therefore practical knowledge of this technical skill can help solve complex problems.

5. Algorithms and Applied Math

Having a firm base in algorithm principles is remarkably essential as it will facilitate comprehending concepts like partial differential equations, quadratic programming, summations, and convex optimization in Artificial Intelligence programs. This skill set serves in the development of revised approaches to meet the difficulties of new problems.

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6. Reinforcement Learning

Reinforcement learning is a form of machine learning training method. It includes rewarding good behaviors and punishing bad ones. A reinforcement learning agent can interpret its environment and take decisive actions and learn through trial and error. Reinforcement Learning is the fundamental basis behind developing deep learning and artificial intelligence to a great degree. It will act as a helping hand to pave the way into fields like self-driving cars and other lines of work in AI and Machine learning, and robotics.

NON TECHNICAL SKILLS

Possessing an abundance of knowledge of the technical aspects of AI is not adequate. One must be capable of communicating it with others and also be equipped with soft skills like the ones mentioned below:

1. Critical Thinking

Critical thinking or analytical thinking is surely a must and an indispensable trait. It is the most important skill that empowers engineers to use logic to solve complicated problems with reasoning and arrive at creative and innovative ideas. The ability to recognize viewpoints before making assumptions or arriving at a conclusion using sound arguments proves that the person did not make hasty decisions.

2. Creativity

In Artificial Intelligence, the algorithm built is the outcome of the creativity of the one who programmed it. The machine demonstrates what it has been entered with. Therefore it can’t do anything imaginative or artistic. It all boils down to the programmer’s creativity. Hence, it is expected to be creative to persist in the industry and build the potentials of previous ideas.

3. Communication

Communication is the key to everything. Even if you have a brilliant idea to program, it will be futile if you do not communicate it to others. Artificial Intelligence and Machine Learning are fields of technology which are difficult to explain to people from other fields if one can’t communicate fluently and clearly. Communicating AI and ML jargon with employees from the finance and sales department can be challenging if one can’t convey the meaning of the terms in layman’s language. Communication doesn’t just limit to talking but also includes body language and tone of the message.

4. Teamwork

The ability to work as a team is a requisite as there might be times when one has to work on a project with other team members. One must be equipped with communication skills, the potentiality to adjust and adapt to other team members’ ideas and requests, and suggestions all take patience to implement AI programs as a team.

5. Domain Knowledge

ML engineers having specialist technical knowledge and caliber in machine learning and artificial intelligence is the first step towards building a career in this industry as businesses and companies always expect one to be well versed. This familiarity in knowledge can assist them in learning how the industry functions, the obstacles faced by business, and the solutions built to resolve them.

6. Decision Making 

A person with an excellent understanding of technical knowledge must also possess decision-making skills. Artificial intelligence in management involves interacting, directing, organizing, and managing computational advancements in addressing complex decision-making dilemmas.

 As the workforce of the AI industry is expanding, it’s high time these skills, as mentioned above, must be honed by technical professionals to pursue a career in this industry. Therefore, it is crucial to upskill oneself to stand out among the other candidates. As everything is online, learners can study at flexible hours in the comfort of their homes from various websites and videos. Some of the links that can be used to learn about these skills are given below:

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