Artificial intelligence (AI) is becoming a greater part of IT jobs. The growing popularity of this technology means additional skills are needed to fill the increasing opportunities in the IT industry.
AI includes machine learning, natural language processing, data mining, image recognition, robotics, and decision-making algorithms. These technologies require computer science, math, engineering, and related skills.
The increasing use of AI elevates the demand for these skills. Working with this technology can lead to ongoing opportunities for career development.
Learn how to navigate AI opportunities in IT jobs and the skills needed for success.
Working with data in AI projects involves data extraction, data analysis, and data visualization. Therefore, knowledge of programming languages such as Python, R, and SQL and experience with tools such as Pandas and NumPy are required.
Machine Learning Skills
Machine learning analyzes data to uncover patterns, create predictions or judgments, and learn from experience. Proficiency in programming languages such as Python, R, and MATLAB and experience with machine learning frameworks such as TensorFlow and Keras are needed to create the algorithms.
Natural Language Processing Skills
Natural language processing analyzes text to understand human language and produce natural, believable answers. Programming skills in Python and Java and familiarity with natural language processing tools such as NLTK and spaCy also are essential.
Machine Learning Engineer
A machine learning engineer uses software, predictive models, and natural language processing to analyze data sets. This professional understands software development methodology, agile practices, software development tools from integrated development environments such as Eclipse and IntelliJ, and the components of a continuous deployment pipeline.
A robotic scientist builds mechanical devices that perform tasks with commands from humans. Writing and manipulating computer programs, collaborating with other specialists, and developing prototypes are required.
A data scientist collects data, then uses machine learning and predictive analytics to analyze and interpret the data. Expertise in using big data platforms and tools such as Hadoop, Pig, Hive, Spark, and MapReduce is required. Fluency in statistical computing languages and programming languages such as SQL, Python, Scala, and Perl also is essential.
A research scientist is an expert in machine learning, natural language processing, computational statistics, applied mathematics, and other AI disciplines. These professionals use deep learning, graphical models, reinforcement learning, computer perception, and data representation in their work.
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