AI Fundamentals: Concepts and Applications

Are you curious about Artificial Intelligence but unsure where to start? This course provides a clear, non-technical introduction to the concepts driving today's smart technology. By the end of this course, you will possess a solid conceptual foundation in AI, distinguishing between different types of machine learning and understanding the basic mechanics of neural networks. You will be prepared to analyze how AI is used across various industries and recognize the ethical challenges involved in its development. What you'll learn: * Understand the history, core definitions, and foundational fields of Artificial Intelligence, including Machine Learning and Deep Learning. * Differentiate between supervised, unsupervised, and reinforcement learning paradigms and their practical uses. * Apply basic prompt engineering techniques to interact effectively with modern generative AI models and large language models (LLMs). * Analyze common data challenges, such as bias and overfitting, and explore fundamental concepts of AI ethics and fairness. * Practice conceptualizing solutions using basic AI models and evaluating model performance metrics. The course begins by defining key terminology and exploring the fundamental components of an AI system. We then move into practical applications, focusing on how data drives learning algorithms and how models are used to solve specific tasks. This course is designed exclusively for absolute beginners with no prior experience in programming, statistics, or data science. If you want a clear, conceptual introduction to AI, this is the place to start. Start reading today and demystify the future of technology.