Course

Python Full Stack Development with AI — Django, FastAPI & AI Agents

A complete Python Full Stack Developer program: Python fundamentals and advanced OOP, backend development with Django and Django REST Framework, high-performance async APIs with FastAPI, database design with PostgreSQL, SQLAlchemy and Redis, a React & TypeScript frontend, AI/LLM integration (OpenAI/Claude/Gemini, streaming, RAG), building real AI agents with LangChain/LangGraph, AI-augmented development with tools like GitHub Copilot and Claude Code, and testing, Docker and cloud deployment — choose from 6 flexible packages, each with mock-interview checkpoints and full career-launch support.

⏱ 14.5 Weeks 🖥 Online 💻 5 Projects ⭐ 4.8/5

🎓 Book Free Demo 📞 +91 75502 51906

INCLUDED AT NO COST

Everything below is included at no extra cost

🚀 FREE!

Live Project(s)

💼 FREE!

Job Reference(s)

🎓 FREE!

Career Guidance

♾️ FREE!

Life Time Support

PACKAGES

We Offer Responsive Packages With An Unexplored Syllabus

Start from zero

⏱ 5 Weeks

★ 4.7/5

Python Backend Foundations

Python fundamentals, OOP and your first Django backend — the on-ramp for a full stack + AI career.

Python Programming Fundamentals
Advanced Python & OOP
Backend Development with Django
Comprehensive Interview Prep & Career Launch

Python, Django, Django ORM, Git & GitHub

View Curriculum →

Most enrolled

⏱ 5.5 Weeks

★ 4.8/5

FastAPI & Modern APIs Specialist

Async-first Python APIs with FastAPI — the stack behind most high-throughput and AI-serving backends.

Python Programming Fundamentals
Advanced Python & OOP
High-Performance APIs with FastAPI
Databases — PostgreSQL, SQLAlchemy & Redis
Comprehensive Interview Prep & Career Launch

Python, FastAPI, Pydantic, PostgreSQL, SQLAlchemy, Redis

View Curriculum →

Complete full stack

⏱ 7.5 Weeks

★ 4.8/5

Python Full Stack Core

Django/FastAPI backend plus a real React & TypeScript frontend — the complete traditional full stack path.

Backend Development with Django
High-Performance APIs with FastAPI
Databases — PostgreSQL, SQLAlchemy & Redis
Frontend Development with React & TypeScript
Comprehensive Interview Prep & Career Launch

Python, Django, Django REST Framework, FastAPI, PostgreSQL, SQLAlchemy, React, TypeScript

View Curriculum →

AI-first specialization

⏱ 7.5 Weeks

★ 4.9/5

AI-Powered Python Development

LLM integration, RAG, real AI agent development and production observability — the strongest differentiator on a Python résumé in 2026.

Advanced Python & OOP
High-Performance APIs with FastAPI
AI & LLM Integration for Web Apps
Building AI Agents with Python
AI-Augmented Python Development
Observability & Edge/Serverless Deployment Platforms
Comprehensive Interview Prep & Career Launch

FastAPI, LangChain, LangGraph, OpenAI/Claude API, vector database, GitHub Copilot, OpenTelemetry, Prometheus, Grafana, Railway, Fly.io, Supabase

View Curriculum →

Flagship

⏱ 17 Weeks

★ 4.9/5

Complete Python Full Stack + AI Flagship Program

Every module — Python, Django, FastAPI, React/TypeScript, databases, AI/LLM integration, AI agent development and production observability — the full modern Python developer path.

All 12 core modules, start to finish
Django + FastAPI backend, PostgreSQL/SQLAlchemy/Redis/pgvector
React & TypeScript full stack frontend
AI/LLM integration and real AI agent development
Testing, Docker, CI/CD, cloud deployment and observability (OpenTelemetry, Prometheus/Grafana, Railway/Fly.io/Supabase)
Comprehensive Interview Prep & Career Launch

Python, Django, Django REST Framework, FastAPI, PostgreSQL, SQLAlchemy, Redis, pgvector, React, TypeScript, LangChain, LangGraph, OpenAI/Claude API, vector database, Docker, GitHub Actions, GitHub Copilot, OpenTelemetry, Prometheus, Grafana, Railway, Fly.io, Supabase

View Curriculum →

Customized

⏱ Custom

★ 4.8/5

Build Your Own (Customized)

Pick exactly the modules you need from the full Python full stack + AI module bank, priced and scheduled to match.

Choose any modules from the shared bank
Custom duration & pricing
Same trainers, same mock-interview access
Comprehensive Interview Prep & Career Launch included

Selected per module

View Curriculum →

LEARNING OUTCOMES

What you will be able to do

Concrete skills, not topic names — each one is something you can show an interviewer.

🎯

Design test cases that find real defects

Boundary values, equivalence classes and edge cases — not just happy-path coverage.

🧪

Automate with a real framework

Page Object Model, reusable helpers and a suite you can actually maintain.

📊

Read a defect report the way a developer will

Reproduction steps, severity and evidence — reports that get fixed, not ignored.

🔧

Build and maintain a test suite

Structure tests so they survive the product changing under them.

🔗

Test APIs, not just interfaces

Postman and REST Assured, including auth flows most courses skip.

📈

Run suites in a CI pipeline

Jenkins and GitHub Actions — tests that run on every commit automatically.

SUCCESS STORIES

Our Students Excelling Worldwide

“

Fathima Rizwana

QA Engineer

🏢 UST

★★★★★

“The Jest module on this course is what actually got me interview-ready for UST — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Ganapathy Subramanian

Associate Software Engineer

🏢 Capgemini

★★★★★

“I came in with almost no exposure to AWS. By the end of the Full Stack Web Development with AI — React, Node.js & Next.js course I was confident enough to talk through it in my Capgemini interview.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Logeswari Palanisamy

Senior QA Engineer

🏢 Accenture

★★★★★

“What stood out on the Full Stack Web Development with AI — React, Node.js & Next.js course was how much real, hands-on time we got with Express instead of just slides.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Charumathi Vishwanathan

Test Lead

🏢 Persistent Systems

★★★★★

“The Full Stack Web Development with AI — React, Node.js & Next.js course covered OpenAI/Claude API in real depth — that's specifically what came up when Persistent Systems interviewed me.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Chandran Velusamy

Senior QA Engineer

🏢 Mindtree

★★★★★

“The Jest module on this course is what actually got me interview-ready for Mindtree — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Padmavathi Govindarajan

Associate Software Engineer

🏢 Cognizant

★★★★★

“The Jenkins module on this course is what actually got me interview-ready for Cognizant — practical, not just theory.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Hema Latha Ravindran

Software Test Engineer

🏢 Deloitte

★★★★★

“I came in with almost no exposure to JUnit 5. By the end of the Java Full Stack Development with AI — Spring Boot, Microservices & React course I was confident enough to talk through it in my Deloitte interview.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Farida Sheik

Associate Software Engineer

🏢 Mphasis

★★★★★

“What stood out on the Java Full Stack Development with AI — Spring Boot, Microservices & React course was how much real, hands-on time we got with Spring Cloud instead of just slides.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

TOOLS & SKILLS

Tools and skills covered

Java

Selenium

TestNG

Postman

Git

Jenkins

JMeter

SQL

🏆

Certificate of Completion

Awarded once every module and project is complete — recognised by our hiring partners.

IS THIS FOR YOU?

Who this course is for

If none of these describe you, tell us — we will point you at a better fit rather than sell you this one.

🎓

Freshers from any engineering branch

🔁

Manual testers hitting the automation ceiling

💻

Support engineers moving into QA

WHY IN FOURCUP?

Software Testing Program

Practical exposure to actual work environment

Work on live, under-construction projects with the exposure of an actual work environment, alongside a freelancer.

Dynamic syllabus structure

Built with multiple industry experts, so students become genuinely fluent in the subject rather than exam-ready.

Expert teachers who are active practitioners too

Mentors bring real industry experience and share their ups, downs and delivery skills, not just slides.

GET STARTED

Get Started Today

Download our Syllabus and review it thoroughly.

Request Call Back / Apply the Enquiry Form.

Join Our Free Demo Class.

Understand the Opportunity.

Join the Training session and become an expert!

KEEP EXPLORING

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AI, ML & Soft Skills

AI, ML & Soft Skills — Complete Career Accelerator

A complete AI/ML Engineer flagship program covering Python and statistical foundations, classical machine learning with scikit-learn, XGBoost and LightGBM, deep learning with PyTorch, the full LLM/GenAI engineering stack (Hugging Face, LangChain, LlamaIndex, fine-tuning, end-to-end RAG pipeline design), vector databases (Pinecone, Weaviate, Chroma, Milvus), MLOps (FastAPI, Docker, Kubernetes, MLflow, Airflow, ONNX), AI agent frameworks (LangGraph, AutoGen, CrewAI, MCP) and responsible AI — folded together with professional communication, behavioral interview mastery and resume/portfolio building, so technical depth and interview readiness are built in one program instead of two.

PythonNumPypandasscikit-learnXGBoost+19 more

15% OFF Flagship AI/ML program

AI, ML & Soft Skills

Soft Skills & Interview Readiness Add-On

A focused Soft Skills & Interview Readiness program covering professional communication for technical roles, group discussion and aptitude fundamentals, the STAR method for behavioral interviews, body language and virtual-interview presence, salary negotiation and offer evaluation, workplace professionalism, and resume/LinkedIn optimization — anchored by a mock interview tailored to your own track (developer, tester or cloud/DevOps), not a generic HR-round rehearsal. Designed to work equally well as a standalone short course or as an add-on graduates of other Fourcup tracks bolt onto their main course.

Zoom/Google MeetSlackLinkedInResume templates

22% OFF Standalone or add-on

AI, ML & Soft Skills

AI & GenAI Add-On — Practical AI Skills for Tech Professionals

Works two ways: as a standalone short course in practical AI/GenAI literacy, or as an add-on for graduates of Fourcup's testing, development or cloud/DevOps tracks who want to layer AI skills onto their main course. Covers what LLMs are and aren't, prompt engineering, evaluating AI output critically, using AI coding/testing/ops assistants day to day, RAG (embeddings, vector databases), calling LLM APIs with streaming and tool-calling, a beginner-level intro to AI agents (LangGraph, AutoGen, CrewAI, MCP), and responsible AI use — scoped for practical fluency, not full ML engineering depth.

ChatGPTClaudeGeminiGitHub CopilotClaude Code+10 more

20% OFF Add-on ready

CURRICULUM

Module by module

Open any module to see exactly what it covers. Nothing is hidden behind a "download the syllabus" form.

5 Weeks

DURATION

MODULES

REAL-TIME PROJECTS

MOCK INTERVIEWS

Start from zero

Python Backend Foundations

⏱ 5 Weeks ★ 4.7

4 modules

Most enrolled

FastAPI & Modern APIs Specialist

⏱ 5.5 Weeks ★ 4.8

5 modules

Complete full stack

Python Full Stack Core

⏱ 7.5 Weeks ★ 4.8

5 modules

AI-first specialization

AI-Powered Python Development

⏱ 7.5 Weeks ★ 4.9

7 modules

Flagship

Complete Python Full Stack + AI Flagship Program

⏱ 17 Weeks ★ 4.9

12 modules

Customized

Build Your Own (Customized)

⏱ Custom ★ 4.8

0 modules

1 Python Programming Fundamentals 1.5 wks ▾

Topic	What You'll Learn
1. Python Syntax & Data Structures	Variables, control flow, lists, dicts, sets and tuples, and writing code that reads the way a working Python team actually writes it.
2. Functions & Modules	Arguments, default/keyword params, scope, and organizing a project into importable modules instead of one long script.
3. Modern Python (3.12+) Features	Structural pattern matching, improved f-strings, and the type-hint and performance improvements that show up in current job descriptions.
4. Virtual Environments & Package Management	venv/uv and requirements/pyproject.toml, so "works on my machine" stops being an excuse.
5. Git & GitHub for Python Projects	Branching, commits, pull requests and resolving a merge conflict without losing anyone's work.
5.1) Real-world starting point	Most beginners learn Python from a script; this module treats every exercise as a small project with its own repo, from day one.

Topic	What You'll Learn
1. Python Syntax & Data Structures	Variables, control flow, lists, dicts, sets and tuples, and writing code that reads the way a working Python team actually writes it.
2. Functions & Modules	Arguments, default/keyword params, scope, and organizing a project into importable modules instead of one long script.
3. Modern Python (3.12+) Features	Structural pattern matching, improved f-strings, and the type-hint and performance improvements that show up in current job descriptions.
4. Virtual Environments & Package Management	venv/uv and requirements/pyproject.toml, so "works on my machine" stops being an excuse.
5. Git & GitHub for Python Projects	Branching, commits, pull requests and resolving a merge conflict without losing anyone's work.
5.1) Real-world starting point	Most beginners learn Python from a script; this module treats every exercise as a small project with its own repo, from day one.

2 Advanced Python & Object-Oriented Programming 1.5 wks ▾

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

3 Backend Development with Django 1.5 wks ▾

Topic	What You'll Learn
1. Django Project Structure & Apps	Settings, URLs, views and the project layout a real Django codebase actually uses.
2. Models & the Django ORM	Defining schema in Python, migrations, querysets, and avoiding the N+1 query trap before it ships.
3. Django Admin	Customizing the built-in admin into a genuinely useful internal tool, not just a demo feature.
4. Building APIs with Django REST Framework	Serializers, viewsets, routers and permissions for a production-shaped REST API.
5. Authentication & Authorization	Session and token-based auth, custom user models, and role-based access control.
5.1) Real-world starting point	Django is still the framework most Python job postings name first for "batteries-included" backend work — this module builds the same mental model a team lead expects on day one.

Topic	What You'll Learn
1. Django Project Structure & Apps	Settings, URLs, views and the project layout a real Django codebase actually uses.
2. Models & the Django ORM	Defining schema in Python, migrations, querysets, and avoiding the N+1 query trap before it ships.
3. Django Admin	Customizing the built-in admin into a genuinely useful internal tool, not just a demo feature.
4. Building APIs with Django REST Framework	Serializers, viewsets, routers and permissions for a production-shaped REST API.
5. Authentication & Authorization	Session and token-based auth, custom user models, and role-based access control.
5.1) Real-world starting point	Django is still the framework most Python job postings name first for "batteries-included" backend work — this module builds the same mental model a team lead expects on day one.

4 Comprehensive Interview Preparation & Career Launch 0.5 wk ▾

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

1 Python Programming Fundamentals 1 wk ▾

Topic	What You'll Learn
1. Python Syntax & Data Structures	Variables, control flow, lists, dicts, sets and tuples, and writing code that reads the way a working Python team actually writes it.
2. Functions & Modules	Arguments, default/keyword params, scope, and organizing a project into importable modules instead of one long script.
3. Modern Python (3.12+) Features	Structural pattern matching, improved f-strings, and the type-hint and performance improvements that show up in current job descriptions.
4. Virtual Environments & Package Management	venv/uv and requirements/pyproject.toml, so "works on my machine" stops being an excuse.
5. Git & GitHub for Python Projects	Branching, commits, pull requests and resolving a merge conflict without losing anyone's work.
5.1) Real-world starting point	Most beginners learn Python from a script; this module treats every exercise as a small project with its own repo, from day one.

Topic	What You'll Learn
1. Python Syntax & Data Structures	Variables, control flow, lists, dicts, sets and tuples, and writing code that reads the way a working Python team actually writes it.
2. Functions & Modules	Arguments, default/keyword params, scope, and organizing a project into importable modules instead of one long script.
3. Modern Python (3.12+) Features	Structural pattern matching, improved f-strings, and the type-hint and performance improvements that show up in current job descriptions.
4. Virtual Environments & Package Management	venv/uv and requirements/pyproject.toml, so "works on my machine" stops being an excuse.
5. Git & GitHub for Python Projects	Branching, commits, pull requests and resolving a merge conflict without losing anyone's work.
5.1) Real-world starting point	Most beginners learn Python from a script; this module treats every exercise as a small project with its own repo, from day one.

2 Advanced Python & Object-Oriented Programming 1.5 wks ▾

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

3 High-Performance APIs with FastAPI 1.5 wks ▾

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

4 Databases — PostgreSQL, SQLAlchemy & Redis 1 wk ▾

Topic	What You'll Learn
1. Relational Schema Design	Normalization, indexes, foreign keys, and designing a schema that survives a growing feature list.
2. SQLAlchemy ORM	Models, sessions, relationships and migrations with Alembic, used the way FastAPI projects actually structure data access.
3. Query Optimization	Reading an EXPLAIN plan, fixing an N+1 query, and knowing when raw SQL beats the ORM.
4. Redis for Caching, Sessions & Queues	Speeding up repeated reads, storing session/token data, and using Redis as a lightweight task queue.
5. pgvector for AI-Ready PostgreSQL	Adding vector search directly on top of the same PostgreSQL database, so embeddings for the AI/RAG modules live alongside the app's relational data instead of a separate vector store.
5.1) Where this shows up in real jobs	A Python backend candidate who can only write basic CRUD queries stalls out in interviews; one who can explain an index, a caching decision, or when to reach for pgvector instead of a dedicated vector database stands out immediately.

Topic	What You'll Learn
1. Relational Schema Design	Normalization, indexes, foreign keys, and designing a schema that survives a growing feature list.
2. SQLAlchemy ORM	Models, sessions, relationships and migrations with Alembic, used the way FastAPI projects actually structure data access.
3. Query Optimization	Reading an EXPLAIN plan, fixing an N+1 query, and knowing when raw SQL beats the ORM.
4. Redis for Caching, Sessions & Queues	Speeding up repeated reads, storing session/token data, and using Redis as a lightweight task queue.
5. pgvector for AI-Ready PostgreSQL	Adding vector search directly on top of the same PostgreSQL database, so embeddings for the AI/RAG modules live alongside the app's relational data instead of a separate vector store.
5.1) Where this shows up in real jobs	A Python backend candidate who can only write basic CRUD queries stalls out in interviews; one who can explain an index, a caching decision, or when to reach for pgvector instead of a dedicated vector database stands out immediately.

5 Comprehensive Interview Preparation & Career Launch 0.5 wk ▾

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

1 Backend Development with Django 1.5 wks ▾

Topic	What You'll Learn
1. Django Project Structure & Apps	Settings, URLs, views and the project layout a real Django codebase actually uses.
2. Models & the Django ORM	Defining schema in Python, migrations, querysets, and avoiding the N+1 query trap before it ships.
3. Django Admin	Customizing the built-in admin into a genuinely useful internal tool, not just a demo feature.
4. Building APIs with Django REST Framework	Serializers, viewsets, routers and permissions for a production-shaped REST API.
5. Authentication & Authorization	Session and token-based auth, custom user models, and role-based access control.
5.1) Real-world starting point	Django is still the framework most Python job postings name first for "batteries-included" backend work — this module builds the same mental model a team lead expects on day one.

Topic	What You'll Learn
1. Django Project Structure & Apps	Settings, URLs, views and the project layout a real Django codebase actually uses.
2. Models & the Django ORM	Defining schema in Python, migrations, querysets, and avoiding the N+1 query trap before it ships.
3. Django Admin	Customizing the built-in admin into a genuinely useful internal tool, not just a demo feature.
4. Building APIs with Django REST Framework	Serializers, viewsets, routers and permissions for a production-shaped REST API.
5. Authentication & Authorization	Session and token-based auth, custom user models, and role-based access control.
5.1) Real-world starting point	Django is still the framework most Python job postings name first for "batteries-included" backend work — this module builds the same mental model a team lead expects on day one.

2 High-Performance APIs with FastAPI 1.5 wks ▾

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

3 Databases — PostgreSQL, SQLAlchemy & Redis 1.5 wks ▾

Topic	What You'll Learn
1. Relational Schema Design	Normalization, indexes, foreign keys, and designing a schema that survives a growing feature list.
2. SQLAlchemy ORM	Models, sessions, relationships and migrations with Alembic, used the way FastAPI projects actually structure data access.
3. Query Optimization	Reading an EXPLAIN plan, fixing an N+1 query, and knowing when raw SQL beats the ORM.
4. Redis for Caching, Sessions & Queues	Speeding up repeated reads, storing session/token data, and using Redis as a lightweight task queue.
5. pgvector for AI-Ready PostgreSQL	Adding vector search directly on top of the same PostgreSQL database, so embeddings for the AI/RAG modules live alongside the app's relational data instead of a separate vector store.
5.1) Where this shows up in real jobs	A Python backend candidate who can only write basic CRUD queries stalls out in interviews; one who can explain an index, a caching decision, or when to reach for pgvector instead of a dedicated vector database stands out immediately.

Topic	What You'll Learn
1. Relational Schema Design	Normalization, indexes, foreign keys, and designing a schema that survives a growing feature list.
2. SQLAlchemy ORM	Models, sessions, relationships and migrations with Alembic, used the way FastAPI projects actually structure data access.
3. Query Optimization	Reading an EXPLAIN plan, fixing an N+1 query, and knowing when raw SQL beats the ORM.
4. Redis for Caching, Sessions & Queues	Speeding up repeated reads, storing session/token data, and using Redis as a lightweight task queue.
5. pgvector for AI-Ready PostgreSQL	Adding vector search directly on top of the same PostgreSQL database, so embeddings for the AI/RAG modules live alongside the app's relational data instead of a separate vector store.
5.1) Where this shows up in real jobs	A Python backend candidate who can only write basic CRUD queries stalls out in interviews; one who can explain an index, a caching decision, or when to reach for pgvector instead of a dedicated vector database stands out immediately.

4 Frontend Development with React & TypeScript for Python Developers 2.5 wks ▾

Topic	What You'll Learn
1. React Fundamentals for a Backend Developer	Components, props, hooks (useState/useEffect) and JSX, taught from the perspective of someone who already thinks in Python.
2. TypeScript Essentials	Types, interfaces and generics, and why a typed frontend pairs naturally with a Pydantic-typed backend.
3. Consuming a Django/FastAPI Backend from React	Fetch/Axios, handling loading and error states, and matching your frontend types to your backend's OpenAPI schema.
4. State Management	Local state vs. React Query/Context for server data, and avoiding the prop-drilling mess that breaks larger apps.
5. Building a Working Full Stack UI	Forms, auth flows and protected routes wired end to end against your own Django/FastAPI API.
5.1) Why this module exists	"Full stack" is not a Python developer who can also write basic HTML — it's someone who can build a real typed UI against their own API and reason about both sides of a bug.

Topic	What You'll Learn
1. React Fundamentals for a Backend Developer	Components, props, hooks (useState/useEffect) and JSX, taught from the perspective of someone who already thinks in Python.
2. TypeScript Essentials	Types, interfaces and generics, and why a typed frontend pairs naturally with a Pydantic-typed backend.
3. Consuming a Django/FastAPI Backend from React	Fetch/Axios, handling loading and error states, and matching your frontend types to your backend's OpenAPI schema.
4. State Management	Local state vs. React Query/Context for server data, and avoiding the prop-drilling mess that breaks larger apps.
5. Building a Working Full Stack UI	Forms, auth flows and protected routes wired end to end against your own Django/FastAPI API.
5.1) Why this module exists	"Full stack" is not a Python developer who can also write basic HTML — it's someone who can build a real typed UI against their own API and reason about both sides of a bug.

5 Comprehensive Interview Preparation & Career Launch 0.5 wk ▾

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

1 Advanced Python & Object-Oriented Programming 1 wk ▾

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

2 High-Performance APIs with FastAPI 1 wk ▾

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

3 AI & LLM Integration for Web Apps 1.5 wks ▾

Topic	What You'll Learn
1. Calling LLM APIs from Python	Integrating OpenAI, Claude and Gemini APIs into a Django/FastAPI backend, and handling keys, rate limits and errors correctly.
2. Streaming Responses	Building a streaming chat-style endpoint with FastAPI and consuming it from the React frontend, token by token.
3. Prompt Engineering for Product Features	Writing prompts that produce a reliable, structured feature output, not a one-off demo answer.
4. Structured Output & Function Calling	Getting an LLM to return validated JSON your Pydantic models can actually consume.
5. Basic RAG (Retrieval-Augmented Generation)	Embedding documents into a vector database and grounding LLM answers in your own app's data.
6. 🚀 Mock Interview Checkpoint	"Add an AI feature to this existing API" live design question, plus streaming/async and prompt-reliability follow-ups.

Topic	What You'll Learn
1. Calling LLM APIs from Python	Integrating OpenAI, Claude and Gemini APIs into a Django/FastAPI backend, and handling keys, rate limits and errors correctly.
2. Streaming Responses	Building a streaming chat-style endpoint with FastAPI and consuming it from the React frontend, token by token.
3. Prompt Engineering for Product Features	Writing prompts that produce a reliable, structured feature output, not a one-off demo answer.
4. Structured Output & Function Calling	Getting an LLM to return validated JSON your Pydantic models can actually consume.
5. Basic RAG (Retrieval-Augmented Generation)	Embedding documents into a vector database and grounding LLM answers in your own app's data.
6. 🚀 Mock Interview Checkpoint	"Add an AI feature to this existing API" live design question, plus streaming/async and prompt-reliability follow-ups.

4 Building AI Agents with Python 1.5 wks ▾

Topic	What You'll Learn
1. What Makes an Agent Different from a Chatbot	Planning, tool use and multi-step reasoning, versus a single prompt-response call.
2. Tool-Calling & Agentic Workflows	Giving an LLM a defined set of Python functions/tools it can call, and structuring the loop that runs them.
3. Building Agents with LangChain/LangGraph	Composing a real agent graph: state, nodes, conditional routing and memory across steps.
4. Building an End-to-End Agent Feature	Shipping one agentic feature inside your full stack project (for example, an agent that queries your own database and takes a bounded action).
5. Evaluating & Guardrailing Agent Output	Testing agent behaviour against known cases, setting boundaries on what an agent is allowed to do, and handling failure gracefully instead of silently.
5.1) Why this module exists	Building an isolated "AI agent" is not the same skill as shipping an agent feature inside a real product with a database, an API and real users — this module is the difference, and it's the single strongest differentiator on a Python résumé in 2026.

Topic	What You'll Learn
1. What Makes an Agent Different from a Chatbot	Planning, tool use and multi-step reasoning, versus a single prompt-response call.
2. Tool-Calling & Agentic Workflows	Giving an LLM a defined set of Python functions/tools it can call, and structuring the loop that runs them.
3. Building Agents with LangChain/LangGraph	Composing a real agent graph: state, nodes, conditional routing and memory across steps.
4. Building an End-to-End Agent Feature	Shipping one agentic feature inside your full stack project (for example, an agent that queries your own database and takes a bounded action).
5. Evaluating & Guardrailing Agent Output	Testing agent behaviour against known cases, setting boundaries on what an agent is allowed to do, and handling failure gracefully instead of silently.
5.1) Why this module exists	Building an isolated "AI agent" is not the same skill as shipping an agent feature inside a real product with a database, an API and real users — this module is the difference, and it's the single strongest differentiator on a Python résumé in 2026.

5 AI-Augmented Python Development 1 wk ▾

Topic	What You'll Learn
1. AI Coding Assistants as a Daily Workflow	Using GitHub Copilot, Claude Code and Cursor inside real Django/FastAPI/React work, not just autocomplete.
2. AI-Assisted Debugging	Turning a stack trace or failing test into a clear root-cause hypothesis faster, and verifying the fix like an editor would.
3. AI-Assisted Code Review	Using an AI assistant to catch style, security and logic issues before a human reviewer sees the pull request.
4. Prompting for Codebase-Aware Suggestions	Getting an assistant to respect your project's existing patterns instead of inventing new ones every time.
4.1) Where this gets misused	Treating AI-generated code as finished code instead of a first draft — every exercise in this module ends with the trainee explaining and defending the change, not just accepting it.

6 Observability & Edge/Serverless Deployment Platforms 1 wk ▾

Topic	What You'll Learn
1. OpenTelemetry & Distributed Tracing	Instrumenting a Django/FastAPI app so a slow request can be traced across the API, database and any LLM call it makes.
2. Prometheus & Grafana for Production Monitoring	Exposing metrics from a FastAPI/Django service, scraping them with Prometheus, and building dashboards and alerts a team actually watches.
3. Edge & Serverless-Hybrid Deployment	Deploying to Railway, Fly.io and Supabase alongside Render/AWS/Azure, and choosing a target based on cold starts, region and cost, not habit.
3.1) Why this module exists	2026 job postings increasingly name OpenTelemetry, or a Railway/Fly.io/Supabase deployment target, directly. A candidate who has only deployed to one traditional cloud pattern reads as narrower than one who can explain the tradeoffs of a modern edge/serverless-hybrid stack.

7 Comprehensive Interview Preparation & Career Launch 0.5 wk ▾

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

1 Python Programming Fundamentals 1.5 wks ▾

Topic	What You'll Learn
1. Python Syntax & Data Structures	Variables, control flow, lists, dicts, sets and tuples, and writing code that reads the way a working Python team actually writes it.
2. Functions & Modules	Arguments, default/keyword params, scope, and organizing a project into importable modules instead of one long script.
3. Modern Python (3.12+) Features	Structural pattern matching, improved f-strings, and the type-hint and performance improvements that show up in current job descriptions.
4. Virtual Environments & Package Management	venv/uv and requirements/pyproject.toml, so "works on my machine" stops being an excuse.
5. Git & GitHub for Python Projects	Branching, commits, pull requests and resolving a merge conflict without losing anyone's work.
5.1) Real-world starting point	Most beginners learn Python from a script; this module treats every exercise as a small project with its own repo, from day one.

Topic	What You'll Learn
1. Python Syntax & Data Structures	Variables, control flow, lists, dicts, sets and tuples, and writing code that reads the way a working Python team actually writes it.
2. Functions & Modules	Arguments, default/keyword params, scope, and organizing a project into importable modules instead of one long script.
3. Modern Python (3.12+) Features	Structural pattern matching, improved f-strings, and the type-hint and performance improvements that show up in current job descriptions.
4. Virtual Environments & Package Management	venv/uv and requirements/pyproject.toml, so "works on my machine" stops being an excuse.
5. Git & GitHub for Python Projects	Branching, commits, pull requests and resolving a merge conflict without losing anyone's work.
5.1) Real-world starting point	Most beginners learn Python from a script; this module treats every exercise as a small project with its own repo, from day one.

2 Advanced Python & Object-Oriented Programming 1.5 wks ▾

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

Topic	What You'll Learn
1. Classes & OOP in Python	Inheritance, composition, dunder methods, and when Python's "everything is an object" model actually matters in real code.
2. Decorators & Context Managers	Writing your own @decorator and with-block, and recognizing why Django and FastAPI lean on both so heavily.
3. Generators & Iterators	Lazy evaluation and memory-efficient data processing, the pattern behind streaming API responses later in the course.
4. Type Hints & Static Typing	Annotating functions and classes so your editor (and FastAPI/Pydantic) catch bugs before runtime.
5. Async/Await Fundamentals	Coroutines, the event loop, and why async matters for I/O-bound work like API calls and database queries.
5.1) Why this module exists	FastAPI, modern Django views and every AI-integration module later in this course assume comfort with async and type hints — this is where that foundation gets built, not assumed.

3 Backend Development with Django 1.5 wks ▾

Topic	What You'll Learn
1. Django Project Structure & Apps	Settings, URLs, views and the project layout a real Django codebase actually uses.
2. Models & the Django ORM	Defining schema in Python, migrations, querysets, and avoiding the N+1 query trap before it ships.
3. Django Admin	Customizing the built-in admin into a genuinely useful internal tool, not just a demo feature.
4. Building APIs with Django REST Framework	Serializers, viewsets, routers and permissions for a production-shaped REST API.
5. Authentication & Authorization	Session and token-based auth, custom user models, and role-based access control.
5.1) Real-world starting point	Django is still the framework most Python job postings name first for "batteries-included" backend work — this module builds the same mental model a team lead expects on day one.

Topic	What You'll Learn
1. Django Project Structure & Apps	Settings, URLs, views and the project layout a real Django codebase actually uses.
2. Models & the Django ORM	Defining schema in Python, migrations, querysets, and avoiding the N+1 query trap before it ships.
3. Django Admin	Customizing the built-in admin into a genuinely useful internal tool, not just a demo feature.
4. Building APIs with Django REST Framework	Serializers, viewsets, routers and permissions for a production-shaped REST API.
5. Authentication & Authorization	Session and token-based auth, custom user models, and role-based access control.
5.1) Real-world starting point	Django is still the framework most Python job postings name first for "batteries-included" backend work — this module builds the same mental model a team lead expects on day one.

4 High-Performance APIs with FastAPI 1.5 wks ▾

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

Topic	What You'll Learn
1. FastAPI Fundamentals & Async Endpoints	Path operations, async def routes, and why FastAPI's async-first design outperforms a sync WSGI stack under concurrent load.
2. Pydantic Data Validation	Request/response models, validators, and catching bad input before it ever reaches your business logic.
3. Dependency Injection	FastAPI's Depends() system for auth, database sessions and shared logic, without repeating yourself across endpoints.
4. Auto-Generated OpenAPI Docs	Getting Swagger/ReDoc documentation for free, and why that matters when a frontend or mobile team is consuming your API.
5. Background Tasks & WebSockets	Handling work after a response is sent, and real-time connections for chat/streaming features.
5.1) When to choose FastAPI over Django	Django wins for a full batteries-included app with an admin panel and ORM baked in; FastAPI wins for high-throughput APIs, microservices and anything that needs to stream — including the AI features built later in this course.

5 Databases — PostgreSQL, SQLAlchemy & Redis 1.5 wks ▾

Topic	What You'll Learn
1. Relational Schema Design	Normalization, indexes, foreign keys, and designing a schema that survives a growing feature list.
2. SQLAlchemy ORM	Models, sessions, relationships and migrations with Alembic, used the way FastAPI projects actually structure data access.
3. Query Optimization	Reading an EXPLAIN plan, fixing an N+1 query, and knowing when raw SQL beats the ORM.
4. Redis for Caching, Sessions & Queues	Speeding up repeated reads, storing session/token data, and using Redis as a lightweight task queue.
5. pgvector for AI-Ready PostgreSQL	Adding vector search directly on top of the same PostgreSQL database, so embeddings for the AI/RAG modules live alongside the app's relational data instead of a separate vector store.
5.1) Where this shows up in real jobs	A Python backend candidate who can only write basic CRUD queries stalls out in interviews; one who can explain an index, a caching decision, or when to reach for pgvector instead of a dedicated vector database stands out immediately.

Topic	What You'll Learn
1. Relational Schema Design	Normalization, indexes, foreign keys, and designing a schema that survives a growing feature list.
2. SQLAlchemy ORM	Models, sessions, relationships and migrations with Alembic, used the way FastAPI projects actually structure data access.
3. Query Optimization	Reading an EXPLAIN plan, fixing an N+1 query, and knowing when raw SQL beats the ORM.
4. Redis for Caching, Sessions & Queues	Speeding up repeated reads, storing session/token data, and using Redis as a lightweight task queue.
5. pgvector for AI-Ready PostgreSQL	Adding vector search directly on top of the same PostgreSQL database, so embeddings for the AI/RAG modules live alongside the app's relational data instead of a separate vector store.
5.1) Where this shows up in real jobs	A Python backend candidate who can only write basic CRUD queries stalls out in interviews; one who can explain an index, a caching decision, or when to reach for pgvector instead of a dedicated vector database stands out immediately.

6 Frontend Development with React & TypeScript for Python Developers 2 wks ▾

Topic	What You'll Learn
1. React Fundamentals for a Backend Developer	Components, props, hooks (useState/useEffect) and JSX, taught from the perspective of someone who already thinks in Python.
2. TypeScript Essentials	Types, interfaces and generics, and why a typed frontend pairs naturally with a Pydantic-typed backend.
3. Consuming a Django/FastAPI Backend from React	Fetch/Axios, handling loading and error states, and matching your frontend types to your backend's OpenAPI schema.
4. State Management	Local state vs. React Query/Context for server data, and avoiding the prop-drilling mess that breaks larger apps.
5. Building a Working Full Stack UI	Forms, auth flows and protected routes wired end to end against your own Django/FastAPI API.
5.1) Why this module exists	"Full stack" is not a Python developer who can also write basic HTML — it's someone who can build a real typed UI against their own API and reason about both sides of a bug.

Topic	What You'll Learn
1. React Fundamentals for a Backend Developer	Components, props, hooks (useState/useEffect) and JSX, taught from the perspective of someone who already thinks in Python.
2. TypeScript Essentials	Types, interfaces and generics, and why a typed frontend pairs naturally with a Pydantic-typed backend.
3. Consuming a Django/FastAPI Backend from React	Fetch/Axios, handling loading and error states, and matching your frontend types to your backend's OpenAPI schema.
4. State Management	Local state vs. React Query/Context for server data, and avoiding the prop-drilling mess that breaks larger apps.
5. Building a Working Full Stack UI	Forms, auth flows and protected routes wired end to end against your own Django/FastAPI API.
5.1) Why this module exists	"Full stack" is not a Python developer who can also write basic HTML — it's someone who can build a real typed UI against their own API and reason about both sides of a bug.

7 AI & LLM Integration for Web Apps 1.5 wks ▾

Topic	What You'll Learn
1. Calling LLM APIs from Python	Integrating OpenAI, Claude and Gemini APIs into a Django/FastAPI backend, and handling keys, rate limits and errors correctly.
2. Streaming Responses	Building a streaming chat-style endpoint with FastAPI and consuming it from the React frontend, token by token.
3. Prompt Engineering for Product Features	Writing prompts that produce a reliable, structured feature output, not a one-off demo answer.
4. Structured Output & Function Calling	Getting an LLM to return validated JSON your Pydantic models can actually consume.
5. Basic RAG (Retrieval-Augmented Generation)	Embedding documents into a vector database and grounding LLM answers in your own app's data.
6. 🚀 Mock Interview Checkpoint	"Add an AI feature to this existing API" live design question, plus streaming/async and prompt-reliability follow-ups.

Topic	What You'll Learn
1. Calling LLM APIs from Python	Integrating OpenAI, Claude and Gemini APIs into a Django/FastAPI backend, and handling keys, rate limits and errors correctly.
2. Streaming Responses	Building a streaming chat-style endpoint with FastAPI and consuming it from the React frontend, token by token.
3. Prompt Engineering for Product Features	Writing prompts that produce a reliable, structured feature output, not a one-off demo answer.
4. Structured Output & Function Calling	Getting an LLM to return validated JSON your Pydantic models can actually consume.
5. Basic RAG (Retrieval-Augmented Generation)	Embedding documents into a vector database and grounding LLM answers in your own app's data.
6. 🚀 Mock Interview Checkpoint	"Add an AI feature to this existing API" live design question, plus streaming/async and prompt-reliability follow-ups.

8 Building AI Agents with Python 1.5 wks ▾

Topic	What You'll Learn
1. What Makes an Agent Different from a Chatbot	Planning, tool use and multi-step reasoning, versus a single prompt-response call.
2. Tool-Calling & Agentic Workflows	Giving an LLM a defined set of Python functions/tools it can call, and structuring the loop that runs them.
3. Building Agents with LangChain/LangGraph	Composing a real agent graph: state, nodes, conditional routing and memory across steps.
4. Building an End-to-End Agent Feature	Shipping one agentic feature inside your full stack project (for example, an agent that queries your own database and takes a bounded action).
5. Evaluating & Guardrailing Agent Output	Testing agent behaviour against known cases, setting boundaries on what an agent is allowed to do, and handling failure gracefully instead of silently.
5.1) Why this module exists	Building an isolated "AI agent" is not the same skill as shipping an agent feature inside a real product with a database, an API and real users — this module is the difference, and it's the single strongest differentiator on a Python résumé in 2026.

Topic	What You'll Learn
1. What Makes an Agent Different from a Chatbot	Planning, tool use and multi-step reasoning, versus a single prompt-response call.
2. Tool-Calling & Agentic Workflows	Giving an LLM a defined set of Python functions/tools it can call, and structuring the loop that runs them.
3. Building Agents with LangChain/LangGraph	Composing a real agent graph: state, nodes, conditional routing and memory across steps.
4. Building an End-to-End Agent Feature	Shipping one agentic feature inside your full stack project (for example, an agent that queries your own database and takes a bounded action).
5. Evaluating & Guardrailing Agent Output	Testing agent behaviour against known cases, setting boundaries on what an agent is allowed to do, and handling failure gracefully instead of silently.
5.1) Why this module exists	Building an isolated "AI agent" is not the same skill as shipping an agent feature inside a real product with a database, an API and real users — this module is the difference, and it's the single strongest differentiator on a Python résumé in 2026.

9 AI-Augmented Python Development 1 wk ▾

Topic	What You'll Learn
1. AI Coding Assistants as a Daily Workflow	Using GitHub Copilot, Claude Code and Cursor inside real Django/FastAPI/React work, not just autocomplete.
2. AI-Assisted Debugging	Turning a stack trace or failing test into a clear root-cause hypothesis faster, and verifying the fix like an editor would.
3. AI-Assisted Code Review	Using an AI assistant to catch style, security and logic issues before a human reviewer sees the pull request.
4. Prompting for Codebase-Aware Suggestions	Getting an assistant to respect your project's existing patterns instead of inventing new ones every time.
4.1) Where this gets misused	Treating AI-generated code as finished code instead of a first draft — every exercise in this module ends with the trainee explaining and defending the change, not just accepting it.

10 Testing, Containers & Deployment 1.5 wks ▾

Topic	What You'll Learn
1. Testing Python Applications with pytest	Unit tests, fixtures, mocking external/LLM API calls, and testing Django/FastAPI endpoints.
2. Containerizing with Docker	Writing a Dockerfile for a Django/FastAPI app, multi-stage builds, and docker-compose for a full local stack.
3. CI/CD with GitHub Actions	Running tests and builds automatically on every push, and blocking a merge on a failing pipeline.
4. Deploying to the Cloud	Shipping a containerized Python app to Render/AWS/Azure, environment configuration and secrets management.
4.1) Real-world starting point	A project that only runs on your laptop isn't a project a hiring manager can evaluate — this module gets every trainee's capstone live on a real URL.

11 Observability & Edge/Serverless Deployment Platforms 1 wk ▾

Topic	What You'll Learn
1. OpenTelemetry & Distributed Tracing	Instrumenting a Django/FastAPI app so a slow request can be traced across the API, database and any LLM call it makes.
2. Prometheus & Grafana for Production Monitoring	Exposing metrics from a FastAPI/Django service, scraping them with Prometheus, and building dashboards and alerts a team actually watches.
3. Edge & Serverless-Hybrid Deployment	Deploying to Railway, Fly.io and Supabase alongside Render/AWS/Azure, and choosing a target based on cold starts, region and cost, not habit.
3.1) Why this module exists	2026 job postings increasingly name OpenTelemetry, or a Railway/Fly.io/Supabase deployment target, directly. A candidate who has only deployed to one traditional cloud pattern reads as narrower than one who can explain the tradeoffs of a modern edge/serverless-hybrid stack.

12 Comprehensive Interview Preparation & Career Launch 1 wk ▾

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

Topic	What You'll Learn
1. Python/Django/FastAPI/AI-Integration Interview Question Bank	Real questions asked for Python full stack and AI-integration roles, reviewed and explained.
2. Resume & LinkedIn Building	Framing Django/FastAPI/React and AI-feature projects the way a hiring manager actually scans a resume.
3. Live Mock Interviews with Feedback	Recorded, one-on-one mock interviews with a working engineer, with specific, actionable feedback.
4. "Design an AI-Powered Feature for This Product" Round	Practising the system-design-style question that separates candidates who bolted on a chatbot from ones who can reason about the whole feature.
5. Salary Negotiation & Offer Evaluation	Reading an offer letter and negotiating from an informed position.
6. Placement Assistance	Referrals and interview opportunities where available, resume review, and continued mentor access after the course ends. Placement assistance is support, not a guarantee of a job or a specific salary — see full terms.
7. 🚀 Final Mock Interview	A full-length, role-realistic Python Full Stack (AI-Integration) Developer interview, end to end.

The curriculum for this package has not been added yet.

WHAT YOU GET

This Course Includes

💻

Online

🎯

14.5 Weeks Duration (Flexible)

🖥

5 Real Industry Projects

🏆

Certificate on Completion

♾️

Lifetime Access to Recordings

💼

Job References to 50+ Companies

🎯

5 Mock Interview Rounds

🗣

Soft Skills & Communication

📱

WhatsApp Batch Support

📥

Brochure & Syllabus Download

🛠 Tools Covered:

PythonDjangoDjango REST FrameworkFastAPIPydanticPostgreSQLSQLAlchemyRedisReactTypeScriptLangChainLangGraphOpenAI/Claude/Gemini APIvector databasesGitHub CopilotClaude CodeCursorpytestDockerGitHub ActionsGit & GitHub

⬇️ Download Syllabus 💬 Enquire About This Course

SUCCESS STORIES

Our Students Excelling Worldwide

“

Fathima Rizwana

QA Engineer

🏢 UST

★★★★★

“The Jest module on this course is what actually got me interview-ready for UST — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Ganapathy Subramanian

Associate Software Engineer

🏢 Capgemini

★★★★★

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Logeswari Palanisamy

Senior QA Engineer

🏢 Accenture

★★★★★

“What stood out on the Full Stack Web Development with AI — React, Node.js & Next.js course was how much real, hands-on time we got with Express instead of just slides.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Charumathi Vishwanathan

Test Lead

🏢 Persistent Systems

★★★★★

“The Full Stack Web Development with AI — React, Node.js & Next.js course covered OpenAI/Claude API in real depth — that's specifically what came up when Persistent Systems interviewed me.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Chandran Velusamy

Senior QA Engineer

🏢 Mindtree

★★★★★

“The Jest module on this course is what actually got me interview-ready for Mindtree — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Padmavathi Govindarajan

Associate Software Engineer

🏢 Cognizant

★★★★★

“The Jenkins module on this course is what actually got me interview-ready for Cognizant — practical, not just theory.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Hema Latha Ravindran

Software Test Engineer

🏢 Deloitte

★★★★★

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Farida Sheik

Associate Software Engineer

🏢 Mphasis

★★★★★

“What stood out on the Java Full Stack Development with AI — Spring Boot, Microservices & React course was how much real, hands-on time we got with Spring Cloud instead of just slides.”

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YOUR MENTOR

Who teaches this course

Sessions are taught by someone who does this work, not by a full-time trainer reading slides.

👨‍🏫

Fourcup Python & AI Faculty

Real Industry Trainers, Not Just Instructors

Every Fourcup trainer has a minimum of 8+ years of total IT industry experience and 5+ years of specific, hands-on experience in the exact tool or domain they teach. They are currently working professionals — the frameworks, edge cases and interview questions taught are what's actually used in live projects today. Mock-interview mentors are the same trainers who conduct real technical interviews at their day jobs.

8+ years experience Active practitioner

SUCCESS STORIES

Our Students Excelling Worldwide

“

Fathima Rizwana

QA Engineer

🏢 UST

★★★★★

“The Jest module on this course is what actually got me interview-ready for UST — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Ganapathy Subramanian

Associate Software Engineer

🏢 Capgemini

★★★★★

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Logeswari Palanisamy

Senior QA Engineer

🏢 Accenture

★★★★★

“What stood out on the Full Stack Web Development with AI — React, Node.js & Next.js course was how much real, hands-on time we got with Express instead of just slides.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Charumathi Vishwanathan

Test Lead

🏢 Persistent Systems

★★★★★

“The Full Stack Web Development with AI — React, Node.js & Next.js course covered OpenAI/Claude API in real depth — that's specifically what came up when Persistent Systems interviewed me.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Chandran Velusamy

Senior QA Engineer

🏢 Mindtree

★★★★★

“The Jest module on this course is what actually got me interview-ready for Mindtree — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Padmavathi Govindarajan

Associate Software Engineer

🏢 Cognizant

★★★★★

“The Jenkins module on this course is what actually got me interview-ready for Cognizant — practical, not just theory.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Hema Latha Ravindran

Software Test Engineer

🏢 Deloitte

★★★★★

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Farida Sheik

Associate Software Engineer

🏢 Mphasis

★★★★★

“What stood out on the Java Full Stack Development with AI — Spring Boot, Microservices & React course was how much real, hands-on time we got with Spring Cloud instead of just slides.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

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BATCHES

When you can start

Weekday, weekend and fast-track options. Seats are limited so sessions stay small enough to ask questions in.

Batch dates for this course have not been added yet.

SUCCESS STORIES

Our Students Excelling Worldwide

“

Fathima Rizwana

QA Engineer

🏢 UST

★★★★★

“The Jest module on this course is what actually got me interview-ready for UST — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Ganapathy Subramanian

Associate Software Engineer

🏢 Capgemini

★★★★★

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Logeswari Palanisamy

Senior QA Engineer

🏢 Accenture

★★★★★

“What stood out on the Full Stack Web Development with AI — React, Node.js & Next.js course was how much real, hands-on time we got with Express instead of just slides.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Charumathi Vishwanathan

Test Lead

🏢 Persistent Systems

★★★★★

“The Full Stack Web Development with AI — React, Node.js & Next.js course covered OpenAI/Claude API in real depth — that's specifically what came up when Persistent Systems interviewed me.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Chandran Velusamy

Senior QA Engineer

🏢 Mindtree

★★★★★

“The Jest module on this course is what actually got me interview-ready for Mindtree — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Padmavathi Govindarajan

Associate Software Engineer

🏢 Cognizant

★★★★★

“The Jenkins module on this course is what actually got me interview-ready for Cognizant — practical, not just theory.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Hema Latha Ravindran

Software Test Engineer

🏢 Deloitte

★★★★★

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Farida Sheik

Associate Software Engineer

🏢 Mphasis

★★★★★

“What stood out on the Java Full Stack Development with AI — Spring Boot, Microservices & React course was how much real, hands-on time we got with Spring Cloud instead of just slides.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

KEEP EXPLORING

Get Your Related Courses

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AI, ML & Soft Skills

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FAQ

Questions people actually ask

If yours is not here, call us — a working engineer will answer, not a call centre.

No questions have been added for this course yet.

SUCCESS STORIES

Our Students Excelling Worldwide

“

Fathima Rizwana

QA Engineer

🏢 UST

★★★★★

“The Jest module on this course is what actually got me interview-ready for UST — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Ganapathy Subramanian

Associate Software Engineer

🏢 Capgemini

★★★★★

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Logeswari Palanisamy

Senior QA Engineer

🏢 Accenture

★★★★★

“What stood out on the Full Stack Web Development with AI — React, Node.js & Next.js course was how much real, hands-on time we got with Express instead of just slides.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Charumathi Vishwanathan

Test Lead

🏢 Persistent Systems

★★★★★

“The Full Stack Web Development with AI — React, Node.js & Next.js course covered OpenAI/Claude API in real depth — that's specifically what came up when Persistent Systems interviewed me.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Chandran Velusamy

Senior QA Engineer

🏢 Mindtree

★★★★★

“The Jest module on this course is what actually got me interview-ready for Mindtree — practical, not just theory.”

🎓 Full Stack Web Development with AI — React, Node.js & Next.js

“

Padmavathi Govindarajan

Associate Software Engineer

🏢 Cognizant

★★★★★

“The Jenkins module on this course is what actually got me interview-ready for Cognizant — practical, not just theory.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Hema Latha Ravindran

Software Test Engineer

🏢 Deloitte

★★★★★

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

“

Farida Sheik

Associate Software Engineer

🏢 Mphasis

★★★★★

“What stood out on the Java Full Stack Development with AI — Spring Boot, Microservices & React course was how much real, hands-on time we got with Spring Cloud instead of just slides.”

🎓 Java Full Stack Development with AI — Spring Boot, Microservices & React

KEEP EXPLORING

Get Your Related Courses

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AI, ML & Soft Skills

AI, ML & Soft Skills — Complete Career Accelerator

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