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Top 5 Jobs In AI and Key Skills Needed To Help You Land One
John McCarthy, an American computer scientist who coined the term artificial intelligence, defines this discipline as the science and engineering of making intelligent machines, especially intelligent computer programs.
While professionals across the globe are worried about robots replacing humans, a Gartner study reports that AI is an emerging field that will create 2.8 million jobs by 2020.
In today’s world, AI capabilities are in high demand across industries—gaming, robotics, face recognition software, weaponry, speech recognition, vision recognition, expert systems, and search engines.
One of the most sought-after jobs in AI, machine learning engineers must possess strong software skills, be able to apply predictive models, and utilize natural language processing while working with massive data sets.
Also, machine learning engineers are expected to know software development methodology, agile practices, and the complete range of modern software development tools right from IDEs like Eclipse and IntelliJ to the components of a continuous deployment pipeline.
Median Salary: $121,106 (Glassdoor) Preferred Qualifications: Hiring companies prefer candidates holding a master's or doctoral degree in computer science or mathematics with working knowledge of modern programming languages like Python, Java, and Scala.
These organizations usually prefer professionals with strong computer programming skills, expert mathematical skills, knowledge of cloud applications and computer languages, excellent communication, and analytical skills, and certifications like machine learning.
Robots can automate jobs, but they require programmers working behind the scenes to ensure they function well.Robotic science is used for multiple functions from space exploration, healthcare, security, too many other scientific fields.
Companies also look for professionals with specializations in advanced mathematics, physical sciences, life sciences, computer science, computer-aided design and drafting (CADD), physics, fluid dynamics and materials science, and related certifications.
Data scientists collect, analyze, and interpret large amounts of data by using machine learning and predictive analytics to gain insights beyond statistical analysis.
In particular, these areas include deep learning, graphical models, reinforcement learning, computer perception, natural language processing, and data representation, graphical models, reinforcement learning, computer perception, natural language processing, and data representation.
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