Complete Computer Vision Bootcamp With PyTorch & Tensorflow

Course Overview

• Course Title: Complete Computer Vision Bootcamp With PyTorch & TensorFlow
• Instructor: Krish Naik
• Target Audience:
  • Beginners looking to start their computer vision journey
  • Data scientists and ML engineers wanting to expand their skill set
  • AI practitioners aiming to master object detection models
  • Researchers exploring computer vision techniques for academic projects
  • Professionals seeking practical experience in deploying CV models
• Prerequisites:
  • Basic knowledge of Python programming
  • Familiarity with fundamental machine learning concepts
  • Basic understanding of linear algebra and calculus
  • Understanding of image data and its structure
  • Enthusiasm to learn computer vision with hands-on projects

Curriculum Highlights

• Key Topics Covered:
  • Introduction to Computer Vision
  • Deep Learning Fundamentals for Computer Vision
  • Convolutional Neural Networks (CNN)
  • Data Augmentation and Preprocessing
  • Transfer Learning for Computer Vision
  • Object Detection Models
  • Image Segmentation Techniques
  • Real-World Projects and Applications
• Key Skills Learned:
  • Master CNN concepts from basics to advanced with TensorFlow & PyTorch
  • Learn object detection models like YOLO and Faster R-CNN
  • Implement real-world computer vision projects step-by-step
  • Gain hands-on experience with data preprocessing and augmentation
  • Build custom CNN models for various computer vision tasks
  • Master transfer learning with pre-trained models like ResNet and VGG
  • Gain practical skills with TensorFlow and PyTorch libraries

Course Format

• Duration: 54 hours on-demand video
• Format: Self-paced online course
• Resources:
  • 2 articles
  • 134 downloadable resources
  • Access on mobile and TV
  • Certificate of completion