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AI for Power BI: Intelligent Analytics & Automated Insights is a specialised programme designed to equip participants with the skills to integrate Artificial Intelligence (AI) capabilities into Microsoft Power BI for smarter, faster, and more accurate decision-making. This programme bridges business intelligence, machine learning, and data-driven automation, enabling learners to transform dashboards from static reports into intelligent analytics platforms.
Participants will learn to use Power BI's native AI features including AI Insights, Auto ML, Cognitive Services, and natural-language analytics, and integrate external AI/ML models for advanced applications. This course is ideal for data analysts, business intelligence professionals, executives, managers, and those working in telecom, finance, logistics, utilities, and public sector roles who want to elevate reporting capabilities with augmented analytics and automated insights.
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This executive-level course develops strategic decision-making capability through the integration of critical thinking frameworks and Artificial Intelligence applications. Participants will explore structured models such as PDCA and the OODA Loop to strengthen analytical reasoning, assess business challenges, and apply AI tools to enhance professional judgment in complex environments.
The course combines simulations, leadership case studies, and guided reflection to help participants recognize cognitive bias, uphold ethical governance, and lead with clarity and confidence. Upon completion, participants will possess the ability to utilize AI as a strategic partner in decision-making and will develop a personalized AI Leadership Roadmap to elevate both individual effectiveness and organizational performance in the digital economy.
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This introductory course provides participants with a comprehensive foundation in Python programming for Artificial Intelligence applications. It covers essential topics such as Python syntax, data types, variables, control structures, functions, and data structures, along with hands-on exercises using tools like Anaconda, Jupyter Notebook, and PyCharm. Participants will learn how to develop, test, and execute Python programs, manipulate files, and apply key programming principles to solve real-world problems.
The course emphasizes practical application through coding exercises and simple AI-related tasks, enabling learners to understand how Python serves as the backbone of data science, deep learning, and automation. Designed for both graduates and working professionals, this course prepares participants to pursue more advanced AI and machine learning pathways while enhancing digital readiness for Industry 4.0.
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This comprehensive training course provides participants with the essential knowledge and practical skills to understand and apply data analytics and machine learning techniques. Participants will explore the fundamentals of data types, cleaning, and visualization before advancing to supervised and unsupervised learning models, including classification, regression, and clustering.
The course also introduces deep learning concepts with convolutional neural networks for image analysis and recurrent neural networks for time series forecasting. Through guided coding sessions and a final hands-on forecasting project, participants will learn how to design, train, and evaluate models using Python. By the end of the course, participants will be able to transform data into actionable insights and develop simple AI-driven applications for business and industry use cases.
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This practical, hands-on course introduces participants to the fundamentals of computer vision for image and video analysis. It covers the key stages of building intelligent vision systems, from digital image fundamentals and feature detection to deep learning with Convolutional Neural Networks (CNNs) and transfer learning. Participants will explore object detection, video tracking, and real-world applications such as defect inspection, safety monitoring, and automation.
The course includes guided mini-projects where learners apply AI models to real data using standard tools and frameworks. By combining theory, coding exercises, and case studies, participants will gain the confidence to design and prototype computer vision solutions that address real organizational challenges and align with Industry 4.0 transformation goals.
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This applied course provides participants with the knowledge and tools to build predictive models for time-dependent data. It introduces the fundamentals of time series analysis, including trend, seasonality, and noise, and progresses to modern forecasting methods using regression, ARIMA concepts, and deep learning architectures such as LSTM and GRU. Participants will gain hands-on experience in data preparation, feature engineering, and model evaluation using real datasets from energy, industrial, and financial domains.
The course emphasizes practical implementation and interpretation of forecasting results, helping participants integrate predictive models into planning, control, or maintenance workflows. By the end of the course, participants will be able to design, train, and evaluate forecasting models that support data-driven decisions across various real-world systems.
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