Learn the latest techniques in computer vision with Python , OpenCV , and Deep Learning!
InstructorJose Portilla
Free
The curriculum emphasizes real-world application through modules on object detection, tracking, and a multi-part capstone project.
Instruction includes specific assessment lectures followed by dedicated solution videos to reinforce learning.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide illustrates automated store technology by showing a street scene with computer vision overlays, including colored bounding boxes and tracking paths around pedestrians.
This screenshot shows a Jupyter Notebook running Python code that uses OpenCV to perform watershed segmentation on a coastal landscape image, displaying both the original photo and the resulting color-coded mask.
This screenshot shows a Jupyter Notebook session where OpenCV is used to convert an image to RGB format before displaying the result with matplotlib.
Learners have reported issues with deprecated functions and incompatible .yml files for current Anaconda versions.
The deep learning section can feel overly complex, and some fundamental concepts rely on external resources like Wikipedia rather than internal theory.
No specific evidence provided for audience fit suitability.