Master the fundamental preprocessing techniques that power modern computer vision systems. Raw visual data is everywhere, but transforming it into actionable insights requires precise preprocessing and motion analysis skills that separate successful AI engineers from the rest.

This Short Course was created to help machine learning and AI professionals accomplish systematic image preprocessing and motion feature extraction for computer vision applications. By completing this course, you'll be able to standardize image data through normalization techniques, convert between color spaces for optimal model performance, and extract motion patterns from video sequences using industry-standard algorithms. These skills directly translate to building more robust computer vision models, improving training efficiency, and developing motion-based applications. By the end of this course, you will be able to: • Apply normalization and color-space conversions to preprocess image data • Apply optical flow and frame differencing techniques to extract motion features from video This course is unique because it combines theoretical understanding with hands-on implementation using real-world datasets, mirroring the exact preprocessing pipelines used by companies like Tesla, Facebook AI Research, and Amazon for their computer vision systems. To be successful in this project, you should have a background in Python programming, basic understanding of machine learning concepts, and familiarity with NumPy and OpenCV libraries.