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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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