Definition of Artificial Intelligence, history and evolution, real-world applications (healthcare, education, business, automation), AI vs ML vs Deep Learning, scope and future of AI-driven technologies.

Supervised and unsupervised learning, datasets and features, model training basics, data preprocessing, classification and clustering overview, introduction to ML workflows without programming.

Understanding neurons, layers, weights & activation functions, forward and backward propagation (conceptual view), types of neural networks, where and how deep learning is used in modern applications.

Text data basics, tokenization, stemming/lemmatization, sentiment analysis, chatbots introduction, translation tools, speech-to-text and text-to-speech fundamentals, modern NLP applications (ChatGPT, assistants).

Using AI-powered tools for writing, design, automation and productivity; ChatGPT, Gemini, Copilot, AI image generators, AI video tools, automated workflows, real-life automation examples for business and daily tasks.

Responsible AI practices, bias in AI, data privacy, AI regulations, job automation risks, future opportunities, emerging AI fields, and safe usage guidelines for AI tools and technologies.

Hands-on use of AI tools to build small tasks such as chatbot interaction, sentiment analysis using online tools, AI-generated presentations, or a mini documentation project showcasing AI capabilities.