Fully Connected Neural Networks with Keras and Python

Deep learning does not have to be intimidating. With Keras, you can build and train powerful neural networks to solve real-world prediction and classification problems using clean, readable Python code. This text-based course guides you from absolute beginner to confidently constructing, training, and evaluating fully connected neural networks. You will learn to prepare your data, structure your network layers, tune hyperparameters, and make accurate predictions. What you'll learn: * Understand the core concepts of deep learning, including neurons, activation functions, and backpropagation. * Prepare and preprocess structured data using modern Python libraries. * Build fully connected neural network architectures layer-by-layer using the Keras API. * Compile models with appropriate loss functions, optimizers, and evaluation metrics. * Train, fine-tune, and evaluate your deep learning models to prevent overfitting. * Save and load trained Keras models for real-world applications. The course begins with essential deep learning terminology and foundational concepts before moving into practical code walkthroughs and written implementation exercises. Designed for aspiring data scientists, software developers, and Python enthusiasts, this course requires only basic Python knowledge and no prior machine learning experience. Start reading today to unlock the power of neural networks in your Python applications.