Preparing Multimodal Data: Vision, Audio, and NLP Pipelines

Raw images, audio clips, and text are only valuable when transformed into formats that AI models can actually use. This intermediate course equips you with the hands-on skills to build multimodal data processing pipelines across three core data types — visual, audio, and language — and to evaluate the AI models trained on them. You will preprocess and enhance image data using normalization, color-space conversion, and quality correction techniques. You will extract motion features from video using optical flow and frame differencing. On the audio side, you will apply spectral and cepstral feature extraction and build augmentation pipelines that improve model robustness. For language, you will fine-tune transformer models on domain-specific datasets and construct end-to-end text preprocessing pipelines using industry-standard tools. Grounded in real-world job tasks from machine learning and AI roles, this course prepares you to take raw, unstructured data and shape it into training-ready inputs — a skill in high demand across AI, computer vision, speech, and NLP teams.