Multi-Class Classification with Keras and Dense Neural Networks

Moving beyond simple binary decisions is a crucial milestone in deep learning. To solve real-world problems like image categorization, document tagging, or product recommendation, your models must learn to choose confidently from three or more distinct classes. This course guides you through the process of building, adapting, and optimizing dense neural networks for multi-category classification. You will learn how to transition your architecture from two-class predictions to multi-class outputs, configure loss functions, and prepare your categorical data for training. What you'll learn: - Understand the core concepts of dense layers and multi-class neural network architectures - Implement appropriate activation functions like Softmax for multi-category outputs - Format categorical target data using one-hot encoding and integer labeling - Configure multi-class loss functions such as categorical and sparse categorical cross-entropy - Train, evaluate, and fine-tune your Keras models to prevent overfitting - Apply modern Python practices, including type hints, to make your deep learning code clean and maintainable You will begin with foundational neural network principles and key classification terminology before moving into step-by-step model building. Through clear written explanations, practical code walk-throughs, and structured exercises, you will gain hands-on experience structuring classification pipelines. This course is designed for beginners who have a basic understanding of Python and want to start building practical deep learning models. No prior machine learning experience is required. Start reading today to build and deploy your own multi-class neural networks.