Image Captioning with TensorFlow & Streamlit

Overview

By completing this course, learners will be able to preprocess image and text datasets, build and evaluate a deep learning model, and deploy a fully functional image captioning application. They will gain hands-on experience in applying tokenization, feature extraction, CNN-RNN architectures, and BLEU score evaluation for accurate caption generation. This course uniquely bridges computer vision and natural language processing, enabling learners to generate meaningful captions for social media images. Unlike traditional AI tutorials, it not only covers dataset preparation and neural network modeling but also demonstrates how to create an interactive Streamlit app and deploy it on AWS EC2 for real-world accessibility. Learners benefit by acquiring both technical depth and practical deployment skills, preparing them for roles in AI development, machine learning engineering, and applied data science. By the end, they will confidently design, test, and launch their own automatic image captioning systems that integrate seamlessly into modern applications.

Syllabus

Data Preparation and Preprocessing

This module introduces learners to the foundations of automatic image captioning by preparing both text and image data. Learners will explore how to access datasets, clean and preprocess captions, and extract meaningful features from images. By the end of this module, they will be able to create structured datasets that combine textual and visual inputs, ensuring data readiness for deep learning models.

Model Development, Evaluation, and Deployment

This module guides learners through the complete model-building lifecycle for automatic image captioning. They will design and train deep learning models, evaluate their performance, and integrate them into an interactive Streamlit application. Finally, learners will test and deploy their app on cloud infrastructure, making their captioning system accessible for real-world use.