Overview

Ever wondered how to detect fish underwater using artificial intelligence?Looking for an exciting and hands-on project in computer vision and deep learning?Welcome to your fully practical mini-course:Fish Detection with Computer Vision and Deep Learning ModelsThis is a no-fluff, real-world project - just code, cameras, and actual results.In this course, you'll:Use Python - beginner-friendly, powerful, and widely used in AIMaster OpenCV - the essential library for image and video processingWork with pre-trained models like YOLO - known for fast and accurate object detectionDetect fish in real-time from underwater cameras or recorded videos Learn how to collect, label, and train custom datasets for specific fish speciesBut it's not just about fish…You'll develop skills that can be applied to:Marine research and environmental monitoringSmart fishing and aquaculture automationUnderwater drones and autonomous exploration systemsWhy choose this project?Build a real-world portfolio project to showcase your skillsNo extra hardware needed - just your laptop and underwater footageGain practical experience in applying AI to challenging, real-world environmentsTackle underwater imaging challenges like light distortion, movement, and turbidityWho is this for?Students in AI, software engineering, marine science, or environmental studiesTech enthusiasts and nature loversAnyone who wants a fun, impressive, and practical AI projectReady to explore the underwater world with AI?Let's dive in and start building!Important Note:Some of the core tools and workflows used in this course - such as Roboflow, labeling, and model training - may also appear in my other courses.However, each course is built around a completely different dataset, project goal, and real-world application.Even when similar tools are used, the challenges, outcomes, and final use cases are entirely unique in each course.This course is self-contained and designed to deliver a specific learning experience related to its own topic.

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