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• Frameworks and libraries (TensorFlow, SciPy, and NumPy): Experienced with leading AI frameworks and libraries.
                   This facilitates implementation of advanced algorithms and data processing.
                   • Neural networks: Hands on experience in designing and training neural networks to develop models for tasks like
                   image and speech recognition.
                    • Machine learning: Proficient in building and deploying machine learning models. Uses techniques to extract valuable
                   insights from data.
                    • Deep learning: Expert in deep learning methodologies. Constructs complex models for sophisticated AI applications.
                    • Shell scripting:  Adept  at  writing  shell  scripts  to  automate  tasks.  Enhances  system  efficiency  and  workflow
                   management.
                    • Cluster analysis: Skilled in performing cluster analysis to identify patterns. Segments data for detailed and actionable
                   insights.

                    • Visualisation of data: Hands on experience in using data visualisation tools like Tableau, Microsoft Power BI, etc. for
                   creating interactive dashboards to support decision-making. Develops comprehensive reports and visualisations for
                   strategic insights.
                    • Knowledge of sensor fusion: For integrating data from multiple sensors (cameras, LiDAR, and so on) to create
                   comprehensive visual understanding systems.

                 Soft Skills/Workplace Skills
                 Soft skills, also known as interpersonal skills or people skills, are non-technical skills that are essential for successful
                 interaction and communication with others, both individually and in groups or teams. These skills are important in every
                 profession and are particularly crucial in roles that involve frequent interaction with clients, customers, colleagues, or the
                 public. Here are some key soft skills:
                    • Communication skills: Effective communication skills are essential for
                   communicating  complicated  technological  concepts  to  non-technical
                   stakeholders and working with multidisciplinary teams.
                    • Teamwork and collaboration: Effective cross-functional teams require
                   strong teamwork and collaboration skills to produce AI solutions.
                    • Problem solving: AI requires the ability to identify, analyse, and resolve
                   challenges effectively.
                    • Decision making: The process of selecting the best course of action from varied options results in the best AI
                   resolution.
                    • Analytical thinking: AI initiates analytical thinking skills to examine and interpret complex information to make
                   informed decisions.
                    • Time management: Efficiently organising and prioritising tasks to maximise productivity and meet deadlines.
                    • Business intelligence:  Utilising  data  analysis  and  insights  to  drive  informed  decision-making  and  strategy
                   development in business.
                    • Critical thinking: The ability to evaluate information objectively and make reasoned judgments or decisions.

                 Baseline Skills
                 Baseline skills refer to fundamental competencies that are essential for understanding, working with, and applying AI
                 technologies effectively. These skills provide a foundation upon which individuals can build more advanced capabilities
                 in AI development, implementation, and research. Here are some key baseline skills in AI:
                    • Linear Algebra: Mathematics dealing with vectors, matrices, and linear transformations, widely used in fields like
                   computer graphics and machine learning.
                    • Probability: Branch of mathematics analysing random phenomena and likelihood of different outcomes, crucial for
                   risk assessment, and statistical inference.
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