Introduction to AI and ML
Understanding what Artificial Intelligence and Machine Learning are, their history, and why they matter today. Difference between AI, ML, and traditional programming.
Core Concepts of AI
Exploring the idea of machines mimicking human intelligence. Overview of natural language processing, computer vision, and decision-making systems.
Fundamentals of Machine Learning
Learning how machines use data to improve performance. Introduction to supervised, unsupervised, and reinforcement learning with simple examples.
Data and Algorithms
Why data is the fuel for AI. Role of datasets, features, and algorithms like regression, classification, and clustering.
AI in the Cloud
How cloud platforms (AWS, Azure, Google Cloud) make AI accessible. Introduction to pre-built AI services like chatbots, translation, and image recognition.
AI Ethics and Risks
Understanding the challenges of AI: bias in algorithms, privacy concerns, and responsible AI usage.
Real-World Applications
Practical examples of AI and ML in healthcare, cybersecurity, finance, and daily life (voice assistants, recommendation engines).