• DEEP LEARNING
• PROJECTS
• COMPUTER VISION
• YOLOV8
• YOLO
• DEEPFAKE
• OBJECT RECOGNITION
• OBJECT TRACKING
• INSTANCE SEGMENTATION
• IMAGE CLASSIFICATION
• IMAGE ANNOTATION
• HUMAN ACTION RECOGNITION
• FACE RECOGNITION
• FACE ANALYSIS
• IMAGE CAPTIONING
• POSE DETECTION/ACTION RECOGNITION
• KEYPOINT DETECTION
• SEMANTIC SEGMENTATION
• Image Processing
• Pixel manipulation
• edge detection
• feature extraction
• Machine Learning
• Pattern Recognition
• Object detection
• classification
• segmentation
• Python
• TensorFlow
• PyTorch
• R-CNN
• ImageNet
• COCO

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).

Who this course is for:

• Beginner ML practitioners eager to learn Deep Learning
• Anyone who wants to learn about deep learning based computer vision algorithms
• Python Developers with basic ML knowledge