// hands_on_coding_lab
// data_science_projects
// team_code_review
INFONEXUS delivers the most comprehensive Python curriculum in Indore — from fundamentals to AI/ML, LangChain agents, FastAPI microservices, and production cloud deployment. Industry-ready from day one.
INFONEXUS Python curriculum mirrors what top tech companies actually use in production — not outdated theory or toy examples.
Every module delivers a working project — REST APIs, ML models, AI agents, data pipelines. Your GitHub portfolio grows with every session.
INFONEXUS teaches LangChain, OpenAI API, Hugging Face, and RAG pipelines — the skills hiring managers at AI-first companies demand in 2025.
Learn Docker, Kubernetes, CI/CD, and cloud deployment from day one — not as an afterthought. Ship real software, not just scripts.
Master Pandas, NumPy, Matplotlib, Scikit-learn, and Tableau integration — complete the full data analyst to data scientist progression.
Learn from developers with hands-on experience at product companies — not just teachers. Real war stories, real code reviews, real mentorship.
INFONEXUS graduates work at TCS, Infosys, startups, and remote-first companies globally. Vijay Nagar's most trusted IT training center since 2016.
INFONEXUS Python developers land roles at ₹6–25 LPA. Here's what you walk away with.
Three progressive tracks from Python syntax to building production AI systems deployed on cloud infrastructure.
Zero to functional programmer. Master Python syntax, data structures, OOP, file handling, and build your first web scraper, API consumer, and data visualization. Perfect for absolute beginners and career switchers. 10 modules with hands-on labs after every session.
Install Python, configure VS Code, understand interpreted execution, variables, data types, type hints, and write your first production-style script.
Master if/elif/else, for/while loops, list comprehensions, lambda functions, *args/**kwargs, decorators basics, and generator expressions for memory-efficient code.
Deep-dive into Python's built-in data structures, time complexity, when to use each, nested structures, and operations needed for coding interviews at MNCs.
Classes, objects, inheritance, polymorphism, encapsulation, dunder methods, class vs static methods, dataclasses, and Pydantic models for data validation.
Read/write text, JSON, CSV, and binary files. Process real-world datasets from government open data, clean messy CSV, and export structured reports.
try/except/finally, custom exceptions, logging with structlog, Python debugger (pdb), and using VS Code debugger — write code that doesn't crash in production.
Make GET/POST/PUT/DELETE requests, handle authentication, parse JSON responses, consume real-world APIs (GitHub, OpenWeather, News API), and build a CLI tool.
Scrape static and JavaScript-rendered websites, handle pagination, extract structured data, store to CSV/JSON, and respect robots.txt — ethical scraping practices.
Load, clean, filter, group, and aggregate real datasets. Create bar charts, line plots, heatmaps, and a complete Exploratory Data Analysis (EDA) report.
Build a complete command-line data analysis tool: fetch live data from an API, process with Pandas, visualize with Rich library, and deploy to GitHub with documentation.
Go from programmer to professional developer. Build production REST APIs, master async programming, implement authentication systems, work with databases via SQLAlchemy, containerize with Docker, and write tests that pass CI/CD. This is what mid to senior Python engineers do daily.
Master async/await, event loops, tasks, asyncio.gather(), ThreadPoolExecutor, multiprocessing — write code that handles 10,000 concurrent requests efficiently.
Build production APIs with FastAPI 0.110+: path operations, request validation with Pydantic v2, dependency injection, background tasks, WebSockets, and auto-generated OpenAPI docs.
ORM models, relationships, migrations with Alembic, async SQLAlchemy sessions, connection pooling, query optimization, and indexing strategies for high-traffic applications.
Implement JWT token auth, OAuth2 with Google/GitHub, password hashing with bcrypt, rate limiting, CORS, HTTPS enforcement, and OWASP Top 10 protections in Python APIs.
Django MVT pattern, models, views, templates, forms, admin customization, class-based views, middleware, signals, and building a complete e-commerce backend.
Implement caching with Redis, build async task queues with Celery, schedule periodic jobs, implement pub/sub messaging, and use Redis as a session store and rate limiter.
Containerize Python applications, multi-stage Dockerfiles, Docker Compose for local dev environments with Postgres + Redis + app containers, and container security best practices.
Unit, integration, and E2E testing with pytest, fixtures, mocking, coverage reporting, factory-boy for test data, GitHub Actions CI, and automatic deployment pipelines.
Deploy FastAPI/Django to AWS EC2, ECS, Lambda; configure RDS, S3, CloudFront; use GCP Cloud Run for serverless containers; implement environment variables and secrets management.
Instrument Python apps with Prometheus metrics, visualize with Grafana dashboards, track errors with Sentry, implement structured logging, and set up on-call alerting.
Repository, Factory, Strategy, and Observer patterns in Python. Domain-Driven Design (DDD), SOLID principles, hexagonal architecture, and refactoring legacy codebases.
Build a complete multi-tenant SaaS backend: auth, billing hooks, async tasks, monitoring, Docker, deployed to AWS with CI/CD — a portfolio project that gets you hired.
The elite program for developers who want to lead AI engineering, data science, or senior backend roles. Master machine learning, deep learning, LangChain agents, vector databases, RAG pipelines, and build AI products that scale to millions of users. Limited batch size for personalized mentorship.
Supervised/unsupervised learning, feature engineering, model selection, cross-validation, hyperparameter tuning with Optuna, and production ML pipelines using sklearn Pipelines.
Neural networks from scratch, CNNs, RNNs, Transformers architecture, transfer learning with HuggingFace, model fine-tuning on custom datasets, and ONNX export for production.
Build LLM-powered apps: chains, agents, memory systems, tools integration, prompt engineering, structured output parsing, and multi-agent orchestration with LangGraph.
Retrieval-Augmented Generation (RAG) architecture, document chunking strategies, embedding models, Pinecone/Chroma/Weaviate vector DBs, semantic search, and hybrid retrieval systems.
Image processing, object detection with YOLOv10, face recognition, OCR with Tesseract, real-time video analysis, and deploy CV models as FastAPI endpoints.
Text classification, NER, sentiment analysis, question answering, summarization, and fine-tune BERT/GPT-2/Llama models on custom domain data using LoRA/QLoRA methods.
Build scalable ETL pipelines with Apache Airflow 2.x, real-time event streaming with Kafka + Python consumers, dbt for data transformations, and data quality with Great Expectations.
Experiment tracking, model registry, A/B testing ML models, feature stores with Feast, model serving with BentoML, and automated retraining pipelines triggered by data drift.
Deploy ML models at scale with Kubernetes, Helm charts for complex deployments, GPU node pools for inference, Horizontal Pod Autoscaler, and Kubeflow Pipelines.
Prompt injection defenses, LLM red teaming, model bias detection with Fairlearn, differential privacy with OpenDP, and GDPR compliance for AI systems in production.
Quantitative finance with QuantLib, backtest trading strategies with Backtrader, integrate Zerodha/Upstox APIs, build options pricing models, and real-time market dashboards.
Build and deploy a complete AI-powered SaaS product: LLM backend, RAG knowledge base, React frontend, subscription auth, monitoring, Kubernetes deployment — production-grade from day one.
Every tool in the curriculum is actively used in production by Python engineers at top tech companies right now.
INFONEXUS Python graduates are working at product companies, AI startups, and leading MNCs across India.
The Advanced track completely transformed how I write Python. LangChain + FastAPI modules were absolutely next-level. Got hired at an AI startup in Pune offering ₹14 LPA — 3 months after completing the course.
I came in knowing zero programming. The Basic track at INFONEXUS is genuinely beginner-friendly — by module 6 I was building APIs. Professional track's MLOps and Kubernetes section is something you'd pay ₹1L for on Udemy.
The capstone project in the Professional track was what made the difference in my interviews. I literally showed live code to the interviewer. Got 3 offers. INFONEXUS is the real deal — no fluff, just actual engineering.
Join 2,000+ developers who built production AI apps, deployed to cloud, and landed high-paying jobs — starting from INFONEXUS, Indore.
Free counselling session — our mentor will contact you within 24 hours.
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