Build 15+ Real-Time Deep Learning(Computer Vision) Projects
Ready to transform raw data into actionable insights?
This project-driven Computer Vision Bootcamp equips you with the practical skills to tackle real-world challenges.
Forget theory, get coding!
Through 12 core projects and 5 mini-projects, you'll gain mastery by actively building applications in high-demand areas:
Object Detection & Tracking:
Project 6: Master object detection with the powerful YOLOv5 model.
Project 7: Leverage the cutting-edge YOLOv8-cls for image and video classification.
Project 8: Delve into instance segmentation using YOLOv8-seg to separate individual objects.
Mini Project 1: Explore YOLOv8-pose for keypoint detection.
Mini Project 2 & 3: Make real-time predictions on videos and track objects using YOLO.
Project 9: Build a system for object tracking and counting.
Mini Project 4: Utilize the YOLO-WORLD Detect Anything Model for broader object identification.
Image Analysis & Beyond:
Project 1 & 2: Get started with image classification on classic datasets like MNIST and Fashion MNIST.
Project 3: Master Keras preprocessing layers for image manipulation tasks like translations.
Project 4: Unlock the power of transfer learning for tackling complex image classification problems.
Project 5: Explore the fascinating world of image captioning using Generative Adversarial Networks (GANs).
Project 10: Train models to recognize human actions in videos.
Project 11: Uncover the secrets of faces with face detection, recognition, and analysis of age, gender, and mood.
Project 12: Explore the world of deepfakes and understand their applications.
Mini Project 5: Analyze images with the pre-trained MoonDream1 model.
Why Choose This Course?
Learn by Doing: Each project provides practical coding experience, solidifying your understanding.
Cutting-edge Tools: Master the latest advancements in Computer Vision with frameworks like YOLOv5 and YOLOv8.
Diverse Applications: Gain exposure to various real-world use cases, from object detection to deepfakes.
Structured Learning: Progress through projects with clear instructions and guidance.
Ready to take your Computer Vision skills to the next level? Enroll now and start building your portfolio!
Core Concepts:
Image Processing: Pixel manipulation, filtering, edge detection, feature extraction.
Machine Learning: Supervised learning, unsupervised learning, deep learning (specifically convolutional neural networks - CNNs).
Pattern Recognition: Object detection, classification, segmentation.
Computer Vision Applications: Robotics, autonomous vehicles, medical imaging, facial recognition, security systems.
Specific Terminology:
Object Recognition: Identifying and classifying objects within an image.
Semantic Segmentation: Labeling each pixel in an image according to its corresponding object class.
Instance Segmentation: Identifying and distinguishing individual objects of the same class.
Technical Skills:
Programming Languages: Python (with libraries like OpenCV, TensorFlow, PyTorch).
Hardware: High-performance computing systems (GPUs) for deep learning tasks.
Additionally:
Acronyms: YOLO, R-CNN (common algorithms used in computer vision).
Datasets: ImageNet, COCO (standard datasets for training and evaluating computer vision models).