Neural networks are at the heart of today’s artificial intelligence, powering technologies like voice recognition, computer vision, autonomous vehicles, and even recommendation systems. By mimicking the structure of the human brain, neural networks have revolutionised the way computers understand patterns, process data, and make predictions.

This course takes you from the ground up, starting with the intuition behind perceptrons and simple neural units. You’ll learn how multiple layers of neurons stack together to form powerful models capable of recognising complex patterns in images, text, and audio. We’ll break down intimidating concepts like activation functions, backpropagation, and gradient descent into step-by-step explanations.

With hands-on coding exercises, you’ll build your first multilayer perceptron (MLP), train a convolutional neural network (CNN) for image classification, and experiment with tuning hyperparameters to improve performance. You’ll also learn about challenges like overfitting and how to address them with techniques such as dropout and regularisation.

By the end of this course, you’ll not only understand how neural networks function but also be able to implement and train them yourself using Python libraries. This is the perfect stepping stone into the world of deep learning and advanced AI.