Python Django + ML Integration – Certification Course

মডিউল – ১ : Introduction to Django & Machine Learning Integration

• Overview of Django framework
  
• Basics of Machine Learning workflows
  
• Benefits of combining Django with ML
  
• Deployment options for ML models in web applications

মডিউল – ২ : Setting Up Django Project

• Installing Django & dependencies
  
• Creating Django project & apps
  
• Configuring project settings for ML integration
  
• Directory structure for ML models

মডিউল – ৩ : Preparing Machine Learning Models

• Importing ML libraries: scikit-learn, TensorFlow, PyTorch, XGBoost
  
• Loading and using pre-trained ML models
  
• Saving models with Pickle / Joblib / ONNX
  
• Model versioning for deployment

মডিউল – ৪ : Integrating ML Models with Django

• Loading ML models in Django views
  
• Creating APIs for ML predictions using Django REST Framework
  
• Input validation and preprocessing in Django
  
• Handling prediction requests and responses

মডিউল – ৫ : Building ML-powered Django Applications

• Creating templates to display predictions
  
• Displaying ML results on HTML pages
  
• Dynamic dashboards using Django + Plotly / Chart.js
  
• User interaction & feedback collection

মডিউল – ৬ : Real-Time ML Predictions

• Using Django Channels for real-time ML predictions
  
• WebSocket integration for live prediction updates
  
• Async processing for heavy ML tasks
  
• Queue management with Celery + Redis

মডিউল – ৭ : Security & Optimization

• Securing ML endpoints in Django
  
• Optimizing model loading & prediction performance
  
• Handling large datasets efficiently
  
• Logging & monitoring ML API requests

মডিউল – ৮ : Deployment of Django + ML Applications

• Deploying Django + ML apps on Heroku / AWS / GCP
  
• Containerized deployment using Docker
  
• Continuous integration & testing for ML updates
  
• Monitoring ML model performance in production

মডিউল – ৯ : Final Project / Capstone

• Build a real-world Django web app with ML integration (e.g., sentiment analysis, recommendation system, predictive analytics)
  
• Include:
  • API endpoints for predictions
    
  • Web dashboard for displaying results
    
  • Real-time updates & user feedback