Artificial Neural Networks for Decision-Making and AI

Artificial neural networks are the driving force behind modern artificial intelligence, transforming how we analyze data and automate complex decisions. Understanding how these networks process information is no longer just for academic researchers—it is a vital skill for anyone looking to navigate the modern technology landscape. This course provides a clear, conceptual introduction to neural networks, guiding you from foundational mathematical ideas to practical implementation concepts without requiring a advanced background in data science. You will learn how biological inspiration translates into computational layers, how networks learn through training, and how these systems are applied to real-world decision-making. What you will learn: • Understand the core architecture of a neural network, including inputs, weights, biases, and activation functions. • Explain how backpropagation and gradient descent optimize network performance during training. • Identify different neural network architectures, such as feedforward, convolutional, and recurrent networks. • Explore modern AI concepts including fine-tuning, basic prompt engineering mechanics, and how neural networks power large language models. • Apply neural network concepts to structure data-driven decision-making problems in your own domain. The course starts with essential terminology and the historical context of AI before moving into step-by-step written explanations of network training, optimization, and modern generative AI applications. This foundational course is designed specifically for beginners, business professionals, and aspiring developers with no prior experience in machine learning. Start reading today to unlock the core principles of neural networks and understand the technology shaping our future.