Python Full Stack with AI Training in Perungudi

• Python 3.12+ syntax, variables, and control flow
• Object-Oriented Programming (Classes, Inheritance, Polymorphism)
• Advanced Data Structures (Lists, Dictionaries, Sets, Tuples)
• File I/O operations and exception handling
• Modular programming and package management (pip)
• Virtual environments and Git version control basics

Beginner Build an automated CLI task manager with local database storage and modular Python code.

• Modern HTML5, CSS3, and JavaScript (ES6+) fundamentals
• React core concepts: Components, Props, and State
• Advanced React Hooks and custom hook creation
• Client-side routing and navigation (React Router)
• Responsive UI design using Tailwind CSS
• Global state management (Redux Toolkit or Zustand)

Beginner to Intermediate Build an interactive e-commerce dashboard UI with dynamic filtering and responsive Tailwind CSS design.

• Introduction to FastAPI (for high performance) and Django (for rapid dev)
• Designing and documenting RESTful APIs
• User authentication and authorization (JWT, OAuth2)
• Middleware, CORS, and request validation (Pydantic)
• Database ORM integration (SQLAlchemy / Django ORM)
• API testing and auto-documentation (Swagger/OpenAPI)

Intermediate Build a secure REST API for a blogging platform with JWT authentication and role-based access.

• Advanced PostgreSQL: Joins, indexing, and query optimization
• NoSQL databases: MongoDB schema design and aggregation
• Database migrations and version control (Alembic)
• Implementing Redis for caching and session management
• Data modeling and relationship mapping
• Handling database transactions and concurrency

Intermediate Build a multi-tenant SaaS database architecture with advanced PostgreSQL queries and Redis caching.

• Microservices architecture vs. Monolithic design
• Asynchronous task queues using Celery and RabbitMQ/Redis
• Docker fundamentals: Images, containers, and Docker Compose
• API Gateways and service-to-service communication
• Introduction to gRPC for high-performance internal APIs
• Basic service discovery and load balancing concepts

Advanced Build an asynchronous email and PDF generation microservice using Celery, RabbitMQ, and Docker.

• NumPy for high-performance numerical computing
• Pandas for data manipulation, cleaning, and transformation
• Exploratory Data Analysis (EDA) techniques
• Data visualization with Matplotlib and Seaborn
• Statistical analysis and probability basics
• Feature engineering and data preprocessing pipelines

Intermediate Build a comprehensive EDA and feature engineering pipeline for real estate data using Pandas.

• Scikit-learn ecosystem and ML workflow
• Supervised learning: Regression and Classification algorithms
• Unsupervised learning: Clustering and Dimensionality Reduction
• Model evaluation metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
• Hyperparameter tuning and cross-validation
• Model serialization and saving (Pickle, Joblib)

Advanced Build a customer churn prediction engine using scikit-learn, hyperparameter tuning, and cross-validation.

• Introduction to Neural Networks and Deep Learning concepts
• Building models with TensorFlow/Keras or PyTorch
• Convolutional Neural Networks (CNNs) for image processing
• Transfer learning using pre-trained models (ResNet, VGG)
• Image data augmentation and preprocessing pipelines
• Model optimization and reducing overfitting

Advanced Build a real-time face mask detection system using CNNs, transfer learning, and webcam integration.

• Natural Language Processing (NLP) fundamentals and Hugging Face
• Prompt engineering techniques for optimal LLM outputs
• LangChain framework for building LLM applications
• Vector databases (Pinecone, ChromaDB) for semantic search
• Retrieval-Augmented Generation (RAG) architecture
• Integrating OpenAI / Anthropic APIs into Python backends

Advanced Build an AI-powered document Q&A chatbot using LangChain, vector databases, and RAG architecture.

• Model serving strategies using FastAPI and Flask
• Experiment tracking and model registry with MLflow
• Building CI/CD pipelines specifically for Machine Learning
• Model monitoring, logging, and detecting data drift
• A/B testing AI models in production
• Containerizing complex ML applications for consistency

Advanced Build an end-to-end ML pipeline with FastAPI serving, MLflow tracking, and automated retraining.

• Cloud fundamentals: AWS/GCP core services (EC2, S3, RDS)
• Kubernetes basics for scaling containerized applications
• Infrastructure as Code (IaC) using Terraform basics
• Deploying Serverless AI functions and background workers
• Application monitoring and logging (Prometheus, Grafana)
• Cloud security best practices and IAM roles

Advanced Build a cloud-native AI SaaS deployment using Kubernetes, Terraform, and Prometheus monitoring.

• Enterprise-grade Full Stack AI Capstone project development
• System design principles for scalable AI applications
• Resume building, LinkedIn optimization, and GitHub profiling
• Mock technical interviews and coding assessments
• Agile/Scrum methodologies and Jira workflow basics
• Behavioral interview preparation and salary negotiation

Expert Build a full-stack AI SaaS product from scratch, complete with cloud deployment and CI/CD.