Image Captioning with TensorFlow & Streamlit

Build an end-to-end automatic image captioning system with TensorFlow and bring it to life through an interactive Streamlit application. This course is designed for learners interested in AI development, machine learning engineering, and applied data science who want practical experience connecting computer vision with natural language processing. You’ll prepare image and caption datasets, clean and tokenize text, structure sequences, and extract meaningful image features. You’ll then implement padding and data generators, construct and train a hybrid CNN-RNN architecture, and evaluate caption quality using the BLEU score. Finally, you’ll integrate the trained model into a Streamlit image captioning app, test it, and deploy it on AWS EC2 for real-world accessibility. What makes this course distinctive is its complete, hands-on workflow: it moves from dataset access and multimodal preprocessing through deep learning model development, evaluation, application building, and cloud deployment. By the end, you’ll be able to design, assess, and launch an automatic image captioning system that generates meaningful captions for social media images and can integrate into modern applications.