Five Major Technical Skills Required to Become an AI Engineer in 2021 Technologies like AI and machine learning are proved as powerful tools to aid business functions. In return, offers effective solutions to solve complex business problems and can act independently in certain situations even software engineers or aspiring AI engineers should not foresee. From using smart assistants like Alexa and Siri to music and media streaming apps like Netflix or Spotify, AI is here to make our lives easier. While people are worried about automation and AI replacing humans, a McKinsey report demonstrated 27 percent of business growth in AI investments. The number of job opportunities across the world continues to increase, more so, there are now five times larger than it used to be in 2013. As a result, specialists in AI are in constant demand across industries related to face recognition software, robotics, gaming, speech recognition, expert system, search engines, vision recognition, weaponry, and more. Therefore, if you’re planning to launch a career in this field, probably now is the right time for a career transition.
AI skills worth learning in 2021 AI is not a fad, it will keep evolving till the foreseeable future. After all, building machine learning models or mimicking the human brain will require more than just theory. The skills required to pursue an AI career vary from one job role to another job role, however, all of them require common technical skills and focus.
Below are the common technical skills required: 1. Programming Languages (Python, R, and Java): You need to have in-depth knowledge in programming languages, most preferably Python, R, and Java. As an aspiring AI engineer, a critical understanding of data structures is a must-have. Python is easy to learn and is also referred to as the lingua franca of machine learning. 2. Mathematics, Linear Algebra, and Statistics: Gaining extensive familiarity with concepts related to vectors, matrices, matrix multiplication is mandatory. How else do you expect to understand and implement different AI models like Naïve Bayes, linear discriminant analysis, and Hidden Markov Models? 3. To gain success in the AI field, you need to have in-depth knowledge about how algorithms work and the theory behind them. Additional knowledge in subjects like Quadratic Programming, Gradient Descent, Partial Differential equation, and Lagrange is an additional advantage.