Learn OpenCV, Keras, object and lane detection, and traffic sign classification for self-driving cars
InstructorSundog Education by Frank KaneInstruction covers essential computer vision foundations including color space conversion, edge detection using Sobel and Canny, and geometric transformations.
The curriculum incorporates specific activities such as detecting lane lines and building CNNs for traffic sign classification to apply technical concepts.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This screenshot shows a Jupyter Notebook running Python code to perform Canny edge detection on an interior car scene, highlighting the contours of passengers and dashboard elements.
This educational slide explains the intuition behind feature detectors like FAST, SURF, ORB, and SIFT, detailing algorithm steps and illustrating pixel analysis on a vehicle.
Instructor Frank Kane is introduced in this frame, standing before a neutral backdrop with on-screen text highlighting his status as a best-selling educator.
Instructor Dr. Ryan Ahmed is introduced in this clear portrait featuring a lower-third graphic that identifies him as a best-selling instructor.
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The content focuses on perception through image processing and machine learning, lacking coverage of live video object detection, path planning, or vehicle control. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
Practical work may rely heavily on following instructor-led notebooks rather than independent problem-solving. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
A learner reported difficulty aligning the suggested Python 3.6 environment with more recent versions like 3.12. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The curriculum aligns with the goal of learning computer vision and machine learning for software engineers, supported by a Python primer.