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• Statistics: Science of collecting, analysing, and interpreting data, used
                 in  science,  engineering,  and  social  sciences  for  making  data-driven
                 decisions.

                 • Signal Processing:  Analysis  and  interpretation  of  signals,  such  as
                 audio and video data, used in telecommunications, medical imaging,
                 and control systems.
                 • Big Data: Management and analysis of large and complex datasets,
                 facilitated  by  technologies  like  Hadoop  and  Spark,  applied  across
                 industries for extracting insights and value from data.


              Your Professional Toolkit
              In addition to learning the requisite skills, AI practitioners should become familiar with prominent AI tools, platforms,
              and programming languages. Few important tools and frameworks are as follows:
                 • Python is a versatile programming language with pre-built libraries for advanced and scientific computing.
                 • R is a computer language used for data collecting, organising, and analysing, with applications in machine learning
                 and statistics.





















                 • Java is a commonly used language in AI to develop intelligent programs, neural networks, and machine learning
                 solutions.

                 • C++ is flexible and object-oriented language, making it ideal for procedural programming and hardware manipulation
                 in AI.
                 • TensorFlow is an open-source machine learning framework that provides tools and libraries for building advanced
                 AI applications.
                 • SciPy is an open-source Python library, used for solving scientific and mathematical problems. It helps users to
                 manipulate and visualise data using various commands.
                 • NumPy  is  a  Python-based  package  used  for  scientific  computing  and  advanced  mathematical  operations  while
                 managing massive data set.
              Aspiring AI professionals can prepare themselves for success in this dynamic and quickly growing sector by learning
              the necessary technical skills and tools. Whether you want to develop AI algorithms or apply AI solutions in real-world
              applications, establishing a solid foundation of skills, and expertise is essential for pursuing fascinating career options
              in artificial intelligence.


                      Traits Required for an AI Professional

              If you are an aspiring AI professional, it's useful to have the following general characteristics:
                 • Be Curious: Curiosity is essential for comprehending the business problems, analysing the data, and seeking potential
                 data applications.

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