Applied Artificial Intelligence: Foundations and Practical Use

Artificial intelligence is rapidly changing every industry, but understanding how to practically apply it can be challenging. Start your journey into applied AI by learning the foundational models and techniques used in modern systems. This course provides a solid conceptual and practical framework for understanding, evaluating, and implementing AI solutions in various application contexts. What you'll learn: * Understand the fundamental differences between various machine learning paradigms, including supervised, unsupervised, and reinforcement learning. * Learn how to structure data and prepare datasets for training and evaluating basic predictive models. * Apply essential principles of prompt engineering to effectively interact with and steer large language models (LLMs) for specific outcomes. * Practice common model evaluation techniques to assess performance and understand limitations in real-world applications. * Configure basic workflows for deploying simple models, introducing key concepts of MLOps for application lifecycle management. The course begins by establishing core terminology and the history of AI, then transitions into practical methods for data preparation, model selection, and deployment strategies. This course is designed for absolute beginners interested in technology, data science, or engineering who want a practical introduction to applied artificial intelligence. No prior programming or statistical knowledge is required. Begin mastering the practical skills needed to utilize AI today.