Back propagation course, in this course we will delve deep into the intricacies of back propagation, understanding its principles, and mastering its application. We will explore how back propagation enables neural networks to learn from data by iteratively adjusting weights to minimize error. Through a series of modules, you will learn the mathematical foundations of back propagation, including the chain rule and gradient descent optimization. We will cover advanced techniques for optimizing back propagation, such as momentum, learning rate schedules, and regularization. Additionally, you will gain practical experience implementing back propagation algorithms in Python, allowing you to train and fine-tune neural networks effectively. Whether you're new to neural networks or seeking to enhance your understanding of back propagation, this course will equip you with the knowledge and skills needed to tackle complex machine learning tasks with confidence.