Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Engineers and developers interested in AI/ML applications
• IT professionals and systems architects involved in AI integration
• Data analysts and business intelligence professionals
• Technical project managers and product owners
• Consultants, researchers, and domain experts exploring AI adoption
• Anyone seeking a solid foundation in artificial intelligence technologies and methods

Note: Basic knowledge of programming and data analysis (e.g., Python, statistics) is recommended.

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

By the end of this course, participants should be able to:

• Understand the core concepts, terminology, and history of artificial intelligence
• Identify and evaluate key AI technologies and their business applications
• Apply machine learning techniques using real-world datasets
• Explore natural language processing, computer vision, and intelligent agents
• Analyze the ethical, social, and legal implications of AI deployment
• Design basic AI models and assess their performance and scalability
• Understand AI system lifecycle, deployment, and governance best practices

DAY 1

Introduction to Artificial Intelligence

• Pre-test
• History and evolution of AI
• AI vs. Machine Learning vs. Deep Learning
• Types of AI: Narrow vs. General AI
• Rule-based systems and knowledge representation
• AI applications across industries
• Workshop

DAY 2

Machine Learning Principles and Techniques

• Supervised vs. unsupervised learning
• Algorithms: regression, classification, clustering
• Model training, testing, and validation
• Overfitting, underfitting, and model performance metrics
• Introduction to Python and Scikit-learn
• Lab Session

DAY 3

Deep Learning and Neural Networks

• Neural network basics: perceptrons, activation functions
• Deep learning architectures: CNNs, RNNs, LSTMs
• Introduction to TensorFlow and PyTorch
• Use cases in image recognition and speech processing
• Transfer learning and fine-tuning pre-trained models
• Lab Session

DAY 4

Natural Language Processing and Computer Vision

• Overview of NLP: tokenization, stemming, sentiment analysis
• Large Language Models (LLMs), GPTs, and transformers
• Image recognition and object detection techniques
• Real-world applications: chatbots, OCR, facial recognition
• Ethical concerns: bias, data privacy, misinformation
• Lab Session

DAY 5

AI in Practice, Governance, and Future Trends

• AI lifecycle: data collection, model development, deployment, monitoring
• Introduction to MLOps and cloud-based AI services
• Explainable AI (XAI) and interpretability tools (e.g., SHAP, LIME)
• AI ethics, fairness, bias mitigation strategies
• Governance, compliance, and regulatory frameworks (e.g., EU AI Act)
• Group Project: Design and present an AI solution for a real-world business scenario
• Course wrap-up, Q&A, feedback, and next steps for learning AI
• Post-test