Build a Basic Neural Network with Python

Unlock the foundational concepts of neural networks by building one yourself, step by step, using Python. This course demystifies the core principles behind artificial intelligence, making complex ideas accessible. By the end of this course, you will have a solid understanding of a basic neural network's architecture and be able to implement its core components in Python, setting the stage for more advanced AI topics. What you'll learn: * Understand the fundamental components of a neural network, including neurons and layers * Implement forward propagation and activation functions using Python and numerical libraries * Grasp the core principles of backpropagation for network training and weight adjustment * Apply Python's type hints and modular design principles for building robust neural network code * Practice preparing data for neural network input and evaluating basic model performance * Configure a simple neural network from scratch to solve a basic classification problem Starting with core definitions and mathematical intuition, the course progressively builds towards writing clear, functional Python code for a basic neural network, including data handling and evaluation. This course is designed for absolute beginners in machine learning and artificial intelligence, with a basic understanding of Python programming. No prior experience with neural networks or advanced mathematics is required. Begin your journey into the exciting world of artificial intelligence by mastering the fundamentals of neural networks.