Introduction to Neural Networks: Building Simple Prediction Models

Understanding how machines learn to make predictions does not require an advanced degree in mathematics. By focusing on the core concepts of linear relationships and basic network structures, you can build a solid foundation in neural networks from scratch. This text-based course guides you through the fundamental mechanics of neural networks, helping you transition from understanding basic mathematical relationships to writing clean, functional Python code that trains a simple predictive model. What you'll learn: Understand the foundational math behind linear relationships and neural network layers; Configure input and output layers to handle simple data structures; Implement forward propagation and basic loss calculations; Train a simple prediction model to find patterns in structured data; Write clean, readable code using type hints and standard scientific libraries. The course starts with essential terminology and foundational definitions before guiding you through step-by-step code implementations. You will read clear explanations, analyze structured code snippets, and reinforce your knowledge through written conceptual exercises. This course is designed for absolute beginners to machine learning and neural networks, with no prior data science experience required. Start reading today to demystify neural networks and build your first predictive model from the ground up.