This course introduces learners to the foundational principles of deep learning, exploring the architecture and functionality of artificial neural networks. Through accessible examples and clear explanations, participants gain an understanding of key components such as inputs, weights, activation functions, and hidden layers. The course covers regression and classification tasks, activation function selection, and the mechanics of forward and backpropagation. Learners will also explore the construction and training of neural networks using both code-based tools like TensorFlow and PyTorch, and no-code platforms such as Google AutoML and Deep Cognition. By the end of the course, participants will understand how to design, interpret, and apply deep learning models to solve real-world problems, from financial forecasting to image recognition.