Image Preprocessing and Building Image Captioning AI Models

Raw images and text descriptions cannot be fed directly into deep learning models without careful preparation. Understanding how to align visual data with natural language is the key to building successful image captioning systems. In this written course, you will progress from understanding foundational computer vision concepts to designing and training your own image captioning pipeline. You will learn to clean, resize, and normalize images, prepare text metadata, and leverage modern deep learning architectures to bridge the gap between sight and language. What you'll learn: - Understand foundational computer vision concepts and image representations in Python. - Apply essential preprocessing techniques including resizing, normalization, and data augmentation. - Tokenize and preprocess text captions using modern natural language processing workflows. - Build dataset pipelines to efficiently feed paired image and text data into neural networks. - Configure deep learning architectures, such as CNN-LSTM and modern Vision Transformer (ViT) hybrids, for image-to-text tasks. - Evaluate model performance using standard natural language generation metrics. The course begins with core terminology and basic image manipulations before guiding you through data pipeline construction, model architecture design, and training processes through clear written explanations and code examples. This course is designed for beginners interested in computer vision and natural language processing, with no advanced mathematical background required. Start reading today to master the foundations of multimodal AI development.