07-01-2026, 21:00 -
[center]![[Obrazek: _60785ebe37175abe128a43bbeed0126c.png]](https://i126.fastpic.org/big/2026/0107/6c/_60785ebe37175abe128a43bbeed0126c.png)
The 50 Practical Computer Vision And Deep Learning Projects
Published 1/2026
Created by William Farokhzad
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 124 Lectures ( 16h 33m ) | Size: 16.4 GB [/center]
Learn to build real-world computer vision systems using modern deep learning techniques
What you'll learn
Implement Image Processing Projects Step-by-Step
Master Python from Beginner to Advanced
Label Images Effectively Using Roboflow
Train YOLO Models for Object Detection
Deep Learning Techniques for Computer Vision
Advanced Image Processing with OpenCV
Build Real-Time Detection Applications
Requirements
A PC or Laptop with Internet Access
Basic Computer Skills - No Prior Coding Required
Python Installed (Setup Guidance Provided)
Interest in Computer Vision and AI
Description
Do you want to truly master Computer Vision and Deep Learning by building real systems, not just watching theory?This comprehensive course is designed to take you from fundamentals to advanced real-world AI applications by building 50 practical, end-to-end computer vision projects using modern deep learning techniques.This is not a theory-heavy course. It is project-driven, hands-on, and industry-focused.You will work on problems inspired by industry, healthcare, agriculture, robotics, security, sports, satellites, and smart cities, gaining the exact skills companies look for in AI and Computer Vision engineers.What Makes This Course Different?50 complete projects - not demos or toy examplesFocus on real-world challenges, datasets, and constraintsLearn how to design, train, evaluate, and deploy vision systemsStrong emphasis on practical workflows and best practicesSuitable for portfolio building, job preparation, and research foundationsEach project is self-contained, with its own dataset, goal, challenges, and final outcome.What You Will LearnThroughout the course, you will learn how to:Use Python for computer vision and deep learning projectsApply OpenCV for image processing and video analysisTrain and fine-tune deep learning models for detection and classificationPrepare, clean, and label datasets correctlyWork with real camera feeds, videos, medical images, aerial imagery, and industrial dataBuild systems that work in real timeUnderstand when and why to choose specific vision techniquesThink like a Computer Vision Engineer, not just a model trainerWho This Course Is ForThis course is ideal for:Students who want practical AI skillsEngineers building real vision systemsResearchers needing strong applied foundationsDevelopers creating portfolio projectsAnyone tired of theory-only AI coursesBasic Python knowledge is helpful, but everything else is taught step by step.50 Hands-On Computer Vision ProjectsAgriculture & NatureTree detection in desert environmentsFruit detection on treesPlant growth monitoring over timePest insect detection on vegetablesRodent detection in natural environmentsBird detection in the wildBear detection in forestsSnake detection on soilScorpion detection in desert terrainBee detection inside beehivesUnderwater & MarineFish detection underwaterShrimp detection underwaterFishing vessel detection at seaUnderwater object recognitionAquatic species classificationMedical & HealthcareSkin lesion detectionLung lesion detection in cancer patientsSpine vertebra detection in MRI imagesSurgical instrument recognitionMicroscopic particle detection in waterIndustry & ManufacturingEgg detection on conveyor beltsBag detection on factory conveyorsBottle cap detection on production linesBolt and nut detectionMechanical component recognitionIndustrial machine part detectionQuality inspection of packaged productsSecurity & SafetyFire detection in visual scenesSafety helmet detection at workplacesGlove detection in laboratoriesMobile phone usage detection at workDangerous gas detection near volcanoesTransportation & InfrastructureRoad pothole detectionTrain container detectionAirport equipment detectionAircraft wheel detectionAircraft loading system recognitionAirport fuel system detectionSports & GamesFoosball ball trackingBasketball player detection from top viewSoccer player detection from aerial viewBackgammon piece detectionAerial & Satellite VisionGround object detection from aerial imageryMoon detection in night sky imagesAerial people detectionContainer detection from aerial footageRetail & Smart SystemsCurrency recognition and verificationPostal package integrity verificationAirport luggage detectionPassenger detection in crowded environmentsWhat You'll Have at the EndBy the end of this course, you will have:50 complete AI projects you can showcaseStrong confidence in computer vision problem solvingA portfolio suitable for jobs, PhD applications, or startupsThe ability to design your own vision systems from scratchImportant NoteSome tools and workflows such as dataset labeling, training pipelines, and evaluation methods may appear across different projects or courses.However:Every project uses a different datasetEvery project solves a unique real-world problemEvery project delivers a distinct learning outcomeThis course is fully self-contained and designed to give you a complete, professional, and practical Computer Vision experience.
Who this course is for
Beginners
Students
AI Developers
Aspiring Data Scientists
Computer Vision Enthusiasts
Beginners in Python
The 50 Practical Computer Vision And Deep Learning Projects
Published 1/2026
Created by William Farokhzad
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 124 Lectures ( 16h 33m ) | Size: 16.4 GB [/center]
Learn to build real-world computer vision systems using modern deep learning techniques
What you'll learn
Implement Image Processing Projects Step-by-Step
Master Python from Beginner to Advanced
Label Images Effectively Using Roboflow
Train YOLO Models for Object Detection
Deep Learning Techniques for Computer Vision
Advanced Image Processing with OpenCV
Build Real-Time Detection Applications
Requirements
A PC or Laptop with Internet Access
Basic Computer Skills - No Prior Coding Required
Python Installed (Setup Guidance Provided)
Interest in Computer Vision and AI
Description
Do you want to truly master Computer Vision and Deep Learning by building real systems, not just watching theory?This comprehensive course is designed to take you from fundamentals to advanced real-world AI applications by building 50 practical, end-to-end computer vision projects using modern deep learning techniques.This is not a theory-heavy course. It is project-driven, hands-on, and industry-focused.You will work on problems inspired by industry, healthcare, agriculture, robotics, security, sports, satellites, and smart cities, gaining the exact skills companies look for in AI and Computer Vision engineers.What Makes This Course Different?50 complete projects - not demos or toy examplesFocus on real-world challenges, datasets, and constraintsLearn how to design, train, evaluate, and deploy vision systemsStrong emphasis on practical workflows and best practicesSuitable for portfolio building, job preparation, and research foundationsEach project is self-contained, with its own dataset, goal, challenges, and final outcome.What You Will LearnThroughout the course, you will learn how to:Use Python for computer vision and deep learning projectsApply OpenCV for image processing and video analysisTrain and fine-tune deep learning models for detection and classificationPrepare, clean, and label datasets correctlyWork with real camera feeds, videos, medical images, aerial imagery, and industrial dataBuild systems that work in real timeUnderstand when and why to choose specific vision techniquesThink like a Computer Vision Engineer, not just a model trainerWho This Course Is ForThis course is ideal for:Students who want practical AI skillsEngineers building real vision systemsResearchers needing strong applied foundationsDevelopers creating portfolio projectsAnyone tired of theory-only AI coursesBasic Python knowledge is helpful, but everything else is taught step by step.50 Hands-On Computer Vision ProjectsAgriculture & NatureTree detection in desert environmentsFruit detection on treesPlant growth monitoring over timePest insect detection on vegetablesRodent detection in natural environmentsBird detection in the wildBear detection in forestsSnake detection on soilScorpion detection in desert terrainBee detection inside beehivesUnderwater & MarineFish detection underwaterShrimp detection underwaterFishing vessel detection at seaUnderwater object recognitionAquatic species classificationMedical & HealthcareSkin lesion detectionLung lesion detection in cancer patientsSpine vertebra detection in MRI imagesSurgical instrument recognitionMicroscopic particle detection in waterIndustry & ManufacturingEgg detection on conveyor beltsBag detection on factory conveyorsBottle cap detection on production linesBolt and nut detectionMechanical component recognitionIndustrial machine part detectionQuality inspection of packaged productsSecurity & SafetyFire detection in visual scenesSafety helmet detection at workplacesGlove detection in laboratoriesMobile phone usage detection at workDangerous gas detection near volcanoesTransportation & InfrastructureRoad pothole detectionTrain container detectionAirport equipment detectionAircraft wheel detectionAircraft loading system recognitionAirport fuel system detectionSports & GamesFoosball ball trackingBasketball player detection from top viewSoccer player detection from aerial viewBackgammon piece detectionAerial & Satellite VisionGround object detection from aerial imageryMoon detection in night sky imagesAerial people detectionContainer detection from aerial footageRetail & Smart SystemsCurrency recognition and verificationPostal package integrity verificationAirport luggage detectionPassenger detection in crowded environmentsWhat You'll Have at the EndBy the end of this course, you will have:50 complete AI projects you can showcaseStrong confidence in computer vision problem solvingA portfolio suitable for jobs, PhD applications, or startupsThe ability to design your own vision systems from scratchImportant NoteSome tools and workflows such as dataset labeling, training pipelines, and evaluation methods may appear across different projects or courses.However:Every project uses a different datasetEvery project solves a unique real-world problemEvery project delivers a distinct learning outcomeThis course is fully self-contained and designed to give you a complete, professional, and practical Computer Vision experience.
Who this course is for
Beginners
Students
AI Developers
Aspiring Data Scientists
Computer Vision Enthusiasts
Beginners in Python
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