Duration:
100 hours
 
Project Overview:
Master image segmentation techniques using deep learning models. This project focuses on dividing images into meaningful segments, enabling applications in medical imaging, autonomous vehicles, and more.
 
Key Learning Outcomes:
Image Preprocessing: Prepare images for segmentation through normalization and augmentation.
Deep Learning Models: Implement and train deep learning models.
Data Augmentation: Enhance model robustness by increasing dataset diversity.
Model Evaluation & Optimization: Compute evaluation metrics.
Data Visualization: Visualize images to interpret model performance.
 
Tools & Libraries:
Programming Language: Python
Libraries: TensorFlow, Keras, PyTorch, OpenCV, matplotlib
 
Deliverables:
Classification Models Notebook: Implementation and training of deep learning classification models.
Evaluation Report: Analysis of model performance with relevant metrics.
Final Report: Comprehensive documentation of methodologies, results, and insights.
 
Who Should Enroll:
Data scientists and AI enthusiasts interested in computer vision.
Individuals looking to specialize in image processing and segmentation techniques.
Professionals aiming to apply classification in various industries like healthcare and automotive.
 
Why This Project? Image segmentation is a critical component in many advanced technologies. By completing this project, you'll gain the expertise to develop models that can accurately segment images, opening doors to numerous applications in cutting-edge fields.