The top skills that are must-haves to make an aspiring career in AI require experience in math

AI technology has been gaining popularity in recent years. This emerging field was expected to create 2.8 million jobs by 2020. The top skills that are must-haves to make an aspiring career in AI include Programming language, Signal processing techniques, Linear algebra and statistics, NLP and Neural networks, and more.

Artificial Intelligence is considered a revolutionary technological advancement and is woven into our everyday lives through social media, gadgets, smart home devices, and banking, among other aspects. AI helps a computer system perform tasks and make decisions or choices independent of human intervention. In today's world, AI is in high demand across industries such as robotics, gaming, search engines, face recognition software, etc. This field requires a great deal of experience in math and science-related topics and offers a lot of job opportunities. Let us now look at the top 10 skills that are must-haves to make an aspiring career in AI.

AI has a wide range of impacts and makes lives easier from automated cars to android systems in many phones, apps, and other electronic devices. To make a prestigious and aspiring career in AI, there are some top most-in-demand skills.

1. Programming Languages

Programming Language is a computer-developed language that helps to easily communicate with different systems and processes and provides instructions to perform. AI career seekers should know different programming languages to carry out roles efficiently. They must be well-versed in programming languages used in AI applications like C++, Java, Python, R, etc

2. Linear Algebra, Calculus, and Statistics

As AI professionals regularly deal with maths and algorithms, good mathematical skills are essential. The AI system is made of mathematical equations and it requires statistical knowledge to decipher the data collected. Using huge mathematical methods, aspirants should decode huge datasets. So, mathematical and statistical skills along with analytical skills are required in solving complex AI.

3. Signal Processing Techniques

Signal processing techniques are concerned with the representation, transformation, and manipulation of signals on a computer. It has a wide range of application systems such as digital communications, medical imaging, consumer electronics, and so on. Therefore, signal processing techniques are necessary for AI professionals in achieving efficiency and productivity in digital communication.

4. NLP and Neural Networks

Neural networks are made to duplicate the way the human brain functions. They are the most precise way of countering problems like translation, speech recognition, image classification, etc. Natural Language Processing in AI systems enables computers to process human language in the form of text or voice data and to understand its full meaning, complete with the speaker or writer's intent.

5. System Design and Architecture

System design or architecture defines the quality attributes of the program being built. It describes the elements of a system, and how they fit and work together to fulfill the requirement of the system. AI systems, if developed without the appropriate architecture will lead to failure due to the system's behavior on data, and misaligned environments for various components. So, to develop a smooth-running AI system, requisite skills in system architecture are necessary.

6. Domain Knowledge

Domain Knowledge is necessary for an AI professional to get engaged with the right stakeholders to understand the business environment and the specific problems that they are required to solve. It helps professionals to frame the right architecture and systems and anticipate future challenges.

7. Communication Skills

Proper communication skills are required for any line of work especially when it comes to explaining AI concepts to laymen. AI professionals focus on solving business problems. Therefore, they are required to coordinate with different teams in the organization to understand the problem better and create better solutions. Good communication skills help to transmit ideas seamlessly and facilitate smooth functioning between teams.

8. Critical Thinking

AI develops and monitors an individual's ability to critically think. Like domain knowledge, critical thinking and analytical skills are equally important. The aspirants and professionals can dissect problems into smaller pieces and ask the right questions and make decisions based on trial and error concerning AI. Critical thinking will aid in producing solutions to address the most complex business problem.

9. Data Modelling

Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. As programs need tons of data to run, data is at the core of AI. Data reviewing and understanding the key relationship between data points is a necessity. Data modeling techniques provide a definite structure for data. AI requires these techniques to recognize these patterns and relations.

10. Language, Audio, and Video Processing

Linguistics and computer science like text, audio, and video are the areas AI professionals get to work with. AI aspirants should be well versed with libraries like Gensim, techniques like summarization sentimental analysis, etc.