Class 014 · PYTHON & AI AGENTS · ENROLLING NOW
Python
+ AI
Agents
A comprehensive journey through modern Python development — from foundational IT and Python syntax through to autonomous agentic frameworks. Master Python fundamentals, OOP, and advanced concepts; ship production APIs with FastAPI; and engineer the 2026 GenAI + Agentic stack — LLM APIs, RAG pipelines, LangGraph, Claude Agent SDK, CrewAI, and the Model Context Protocol.
3mo
duration
30+
modules
4.7/5
class rating
100k+
enrolled
Where our Python Training alumni work
Microsoft
Amazon
SalesforceAI Engineer
Deloitte
Infosys
Accenture
TCS
Wipro
Capgemini
Cognizant
HCL
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with
Four things every Python grad walks away with.
Most Python courses stop at “list comprehensions and Flask.” Ours makes you ship production async FastAPI services, governed PostgreSQL data layers, and an autonomous Coding Agent that hiring teams across BFSI backend, AI-first product firms, GCCs, and ML platform teams can verify on day one of the interview loop.
01
Agent-Ready Python skills
Full-stack Python fluency — from syntax to production. OOP, decorators, generators, async,
FastAPI, PostgreSQL, NumPy, Pandas — and the 2026 AI engineering layer: LLM APIs, RAG, vector databases,
LangGraph, Claude Agent SDK, MCP. Not just a language — an engineering discipline.
02
A shipped Coding Agent project
The named brochure project — a production-deployed autonomous coding agent. LangGraph state
machine, Claude Agent SDK for tool use, MCP servers for code search and execution, FastAPI backend,
observability with LangSmith, public verification URL, GitHub repo with full architecture docs.
03
Verifiable credential
2026 Agent-Ready rubric mapped to PCEP / PCAP (Python certifications), PostgreSQL Associate,
and the emerging AI Engineer body of knowledge — graded 1–5, with a public verification URL recruiters can
check in 30 seconds. Built around real GitHub artefacts, not multiple-choice tests.
04
Direct placement pipeline
GitHub + LinkedIn portfolio rewrite, Python-tuned resume rebuild, and warm intros into our
1,000+ hiring partners actively staffing Python Developer, Backend Engineer, AI Engineer, ML Engineer, GenAI
Engineer, and Agentic AI Engineer roles across BFSI, GCCs, and product firms.
5 MONTHS · FOUR PHASES · ONE CODING AGENT
From “prints hello world” toships autonomous AI agents..
Weeks 1–2 build IT & AI foundations and your first Python workstation. Weeks 3–8 are the Python core — syntax, data structures, OOP, advanced concepts — paired with PostgreSQL for production data work. Weeks 9–14 layer in Python data libraries (NumPy, Pandas, Matplotlib) and FastAPI for production APIs. Weeks 15–20 ship the GenAI + Agentic AI track — frontier models, prompt engineering, RAG pipelines, LangGraph, Claude Agent SDK, MCP, and your deployed Coding Agent.
Weeks 1–2 · Foundations
IT & AI Foundations + Python Setup
- Application lifecycle, Agile/Scrum, Waterfall vs Agile
- CPU/GPU, IaaS/PaaS/SaaS, cloud computing models
- Introduction to AI, ML, Deep Learning, Generative AI, LLMs
- Python interpreter installation, VS Code IDE setup, virtual environments
YOU SHIPA configured Python development environment, a first script using Python’s 35
keywords and built-in data types, and a Scrum board ready for the 20-week sprint cadence.
Weeks 3–8 · Python Core + SQL
Python Mastery + PostgreSQL
- Python syntax, data types, conditionals, loops, control flow
- Data structures — lists, tuples, dictionaries, sets, comprehensions
- OOP — classes, inheritance, polymorphism, magic methods, abstraction
- Advanced Python — exception handling, decorators, generators, context managers
- PostgreSQL — DDL, DML, JOINs, window functions, CTEs, PL/pgSQL, triggers
YOU SHIPA complete Python module library (OOP + functional patterns) plus a
normalised PostgreSQL schema with stored procedures and indexed query plans — ready to power the FastAPI
service that comes next.
Weeks 9–14 · Libraries + FastAPI
Python Libraries + Modern API Engineering
- NumPy — arrays, broadcasting, ufuncs, linear algebra, vectorisation
- Pandas — DataFrames, indexing, groupby, merging, time series
- Matplotlib, Seaborn, Plotly — static and interactive visualisations
- FastAPI — async endpoints, Pydantic validation, JWT auth, Swagger UI
- 3x performance over Flask, automatic OpenAPI documentation, type-safe APIs
YOU SHIPA production FastAPI service backed by PostgreSQL, with NumPy/Pandas data
processing endpoints and a Plotly-powered analytics dashboard — deployed to a public URL with full async
support and Swagger docs.
Weeks 15–20 · GenAI + Agentic AI
Master the 2026 GenAI + Agentic AI stack — from frontier model selection to a production-deployed autonomous Coding Agent.
Build with LLM APIs from OpenAI, Anthropic, Google GenAI, and DeepSeek. Master prompt engineering (zero-shot, few-shot, CoT, ReAct) and context engineering — the 2026 frontier discipline. Engineer production RAG pipelines with ChromaDB, Pinecone, Qdrant, and pgvector — including hybrid search and agentic re-ranking. Master the 2026 production agent stack — LangGraph 1.0 (#1 production default), Claude Agent SDK (#2 MCP-native), CrewAI (#3 multi-agent crews). Wire it all through the Model Context Protocol (MCP) — 200+ server implementations, 97M+ monthly SDK downloads. Final project — a deployed Coding Agent with FastAPI backend, LangSmith observability, and a public verification URL.
Partner orgs
(2026)48
Projects
deployed280+
→ Placement
offers82%
Course curriculum
Seven sections. 65+ modules. The AI-native Python Training stack.
Jump to any section on the left. Click a module to see topics, hands-on lab, and key technologies.
01
Fundamentals of IT & AI
Foundational track building the conceptual bedrock for every data professional —
application lifecycle, Agile/Scrum, computing infrastructure, AI/ML/Generative/Agentic AI
fundamentals, and real-world digital systems.
Application fundamentals — what applications are, their types, web architecture
Web Technologies — Frontend (HTML, CSS, JavaScript, React) and Backend (Python,
Java, Node.js)
Database Systems — SQL (MySQL, PostgreSQL) and NoSQL (MongoDB) for data
management
The seven SDLC phases — Planning, Analysis, Design, Implementation, Testing,
Deployment, Maintenance
How each phase builds on the previous for systematic development of robust
applications
Understanding the SDLC is fundamental to managing complex software projects
Methodology Evolution — Waterfall vs Agile, the Agile mindset
Popular frameworks — Scrum, Kanban, Extreme Programming (XP)
Scrum Roles — Product Owner, Scrum Master, Development Team
Scrum Events — Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective
Scrum Artifacts — Product Backlog, Sprint Backlog, Increment deliverables
User Stories — Epics, Themes, Acceptance Criteria
Estimating user stories and managing backlogs with Google Sheets and Azure Boards
Transparent communication, progress tracking, collaborative planning across
distributed teams
CPU Technology — general-purpose computing, sequential operations, multi-core
parallel processing
GPU Technology — parallel processing for AI training, data processing, scientific
simulations
IaaS — Infrastructure as a Service: virtualised servers, storage, networking
PaaS — Platform as a Service: development and deployment environments without
infrastructure management
SaaS — Software as a Service: ready-to-use applications via web browser
Understanding fundamental computing technologies and cloud service models is
essential for navigating today's digital landscape
Machine Learning — algorithms that improve through experience, learning from data
patterns without explicit programming
Deep Learning — neural networks with multiple layers that process complex
patterns in large datasets
Generative AI — systems that create new content, from text to images, based on
learned patterns
Large Language Models — LLMs that process and generate human-like text, powering
chatbots, content creation, and translation
Image Generation — AI models creating original images from text descriptions
Customer Relationship Management — CRM systems centralising customer
interactions, sales pipelines, marketing campaigns
Human Resource Management Systems — HRMS solutions for recruitment, payroll,
performance management, employee records
Retail & E-Commerce — digital commerce platforms integrating inventory, payment
processing, customer analytics
Healthcare Applications — medical software managing patient records, appointment
scheduling, diagnostic support
Understanding real-world digital systems demonstrates how the technology stack
delivers measurable business value
02
Python for AI & Data
The centrepiece of the programme. From basic syntax through advanced OOP and
metaprogramming patterns — covering data structures, file handling, exception handling, decorators,
generators, and context managers. By the end of this section you write production-grade Python, not
classroom snippets.
Python interpreter installation for Windows and Mac
Visual Studio Code IDE configuration for optimal development experience
Python's 35 essential keywords, identifiers, and naming conventions
Variables and memory management fundamentals
Simple and complex data types, type conversion and casting
Arithmetic, comparison, and logical operators
User input with input() function
Conditional statements — if, elif, else, match-case
while and for loops with range() function
break, continue, and pass statements
String definition, syntax rules, and immutability concept
Positive and negative indexing
Slicing with start:end:step notation
Concatenation and repetition operations
String formatting with f-strings and .format()
Case conversion — .upper(), .lower(), .title()
Search methods — .find(), .index(), .count()
Checking methods — .isalpha(), .isdigit(), .isspace()
Trimming — .strip(), .lstrip(), .rstrip()
Replacement, .split()/.join() operations, and alignment methods
Lists — mutable sequences: creation, indexing, and slicing
Adding elements — .append(), .insert(), .extend()
Removing elements — .remove(), .pop(), .clear()
Sorting and reversing operations
List comprehensions for elegant data transformation
Tuples — immutable sequences: creation and basic operations
Tuple packing and unpacking
Performance advantages over lists and use cases for immutability
Comparison with lists for choosing appropriate structures
Dictionaries — key-value storage: creation, access, and operations
Methods — .keys(), .values(), .items()
Dictionary comprehensions for concise data construction
Nested dictionaries for structured data and fast key-based lookups
Sets — unique collections and the UUU properties (Unique, Unordered, Unindexed)
Mathematical operations — union, intersection, difference, symmetric difference
Subset and superset checks
Frozen sets for immutable, hashable collections
Collections module — namedtuple for readable tuples
Counter for counting hashable objects and defaultdict for default values
deque for efficient queue operations
Iteration protocol enabling custom iterators
Generators using yield statements for memory-efficient data streaming
Generator expressions and generator pipelines
Lambda functions for anonymous functions
Higher-order functions — map(), filter(), reduce()
Generator pipelines process data streams without loading entire datasets into
memory
Function definition, parameters, and return values
Default arguments, *args, **kwargs
Variable scope — local, enclosing, global, built-in (LEGB rule)
First-class functions and higher-order patterns
Recursion and recursive design patterns
Function annotations and type hints (Python 3.5+)
Documenting functions with docstrings (PEP 257)
Built-in modules that ship with Python
User-defined modules organising custom code and external packages
Importing techniques — import module, from module import name, aliasing with as
Namespace management and package structure
__init__.py files defining package behaviour and nested structures
pip installs external packages from PyPI
requirements.txt files managing project dependencies
Virtual environments for reproducible builds
Common built-in modules — math, random, datetime, os, sys
Popular external packages — requests, pandas, numpy
CRUD operations with open() function and various file modes — r, w, a, b, x, +
Reading line by line vs reading entire file
Working with paths using pathlib
os and shutil modules for file path operations and directory manipulation
CSV module — reader, writer, DictReader, DictWriter classes
CSV integration with data analysis workflows
JSON operations — dump(), dumps(), load(), loads() for serialisation and
deserialisation
JSON for configuration files, API responses, and structured data storage
Exception Handling — try-except-else-finally blocks, catching specific exceptions
Raising and re-raising exceptions, custom exception classes, built-in exception
types
Decorators — function decorators, decorators with arguments, multiple decorators,
class decorators
Decorator applications — logging, timing, authentication, caching
Generators deep dive — generator expressions, infinite generators, memory
efficiency
Advanced iteration patterns for large datasets
Context Managers — with statement, custom context managers via __enter__ and
__exit__
Resource management and automatic cleanup
These four patterns separate scripting Python from production Python
Classes & Objects — classes define blueprints; objects represent instances with
attributes and methods
Methods — instance methods, class methods (@classmethod), static methods
(@staticmethod)
Special Methods — magic methods like __str__, __repr__, __len__, __init__,
__del__
Instance variables vs class variables
Encapsulation — access modifiers (public, protected _, private __) control data
visibility
Inheritance — single, multi-level, and multiple inheritance enable code reuse
super() function and method overriding
Abstraction — abstract classes and methods (from abc module) define interfaces
Polymorphism — method overriding and duck typing enable flexible object
interactions
OOP is the cornerstone of production Python — FastAPI, Django, and LangChain are
all built on these four pillars
03
SQL for AI & Data
PostgreSQL from foundations through advanced programming — database design,
complex queries, joins, transactions, stored procedures, triggers, and optimization. Skills to
design, query, and manage enterprise databases that power your FastAPI services and AI applications.
ACID Properties — Atomicity ensures complete transactions
Consistency maintains data validity and Isolation prevents interference between
concurrent transactions
Durability guarantees persistence after commit
PostgreSQL installation across Windows, Mac, and Linux platforms
Configuration of psql command-line tool and pgAdmin 4 graphical interface
Connecting via DBeaver and other clients
Database objects — databases contain schemas, which organise tables
Numeric data types — INTEGER, DECIMAL, FLOAT
Character data types — VARCHAR, TEXT, CHAR
Date/Time, Boolean, and special types — JSON, ARRAY, UUID
Constraints — PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT
SELECT statements and column projection
WHERE clauses with operators and conditions
Built-in functions — string, numeric, date, conditional
Aggregates — COUNT, SUM, AVG, MIN, MAX
GROUP BY for aggregation across categories
HAVING for post-aggregation filtering
Window functions — ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, running totals
JOIN operations — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF
Window functions are the single most underused SQL feature — master them and
you'll solve analytics problems in one query that others write 50 lines for
Subqueries — scalar, row, and table subqueries
CTEs (Common Table Expressions) — WITH clauses for readable complex queries
Recursive CTEs for hierarchical data
Set operators — UNION, UNION ALL, INTERSECT, EXCEPT
DML — INSERT, UPDATE, DELETE patterns and best practices
Transactions — BEGIN, COMMIT, ROLLBACK, savepoints
Isolation levels — Read Uncommitted, Read Committed, Repeatable Read,
Serializable
Concurrency control — locks, deadlocks, MVCC
ALTER TABLE for schema evolution
Indexes — B-tree, Hash, GiST, GIN; when to use each
Creating, modifying, and dropping indexes
Views — virtual tables, materialized views, refresh strategies
Stored functions — CREATE FUNCTION in PostgreSQL
PL/pgSQL — variables, control structures, loops, exception handling
Stored procedures vs functions
Triggers — BEFORE, AFTER, INSTEAD OF triggers; row-level vs statement-level
Exception handling and error management in stored code
ER (Entity-Relationship) modelling — entities, attributes, relationships,
cardinality
Normalization — 1NF, 2NF, 3NF (and when to deliberately denormalize)
Design best practices for OLTP vs analytics workloads
Naming conventions, surrogate vs natural keys
Query plan analysis with EXPLAIN and EXPLAIN ANALYZE
Index strategies — selectivity, covering indexes, multi-column indexes
Query rewriting for performance
Statistics, VACUUM, ANALYZE
Partitioning for large tables
An index makes reads fast and writes slow — never add an index without measuring
both
04
Python Libraries for AI & Data
The data engineer's toolkit. NumPy provides the numerical computing foundation,
Pandas the standard for tabular data manipulation, and Matplotlib, Seaborn, and Plotly the complete
visualization spectrum — from static publication-quality charts to fully interactive web-ready
dashboards.
Array creation — zeros(), ones(), arange(), linspace(), random() functions
Arrays with specific patterns or values
Indexing, slicing, fancy indexing
Reshaping and transposing array structures
Broadcasting rules enable operations on arrays of different shapes
Eliminating explicit loops for vectorised computations
Performance gains of 10-100x over equivalent Python loops
Universal functions (ufuncs) perform element-wise operations efficiently
Linear algebra operations — dot products, matrix multiplication
Boolean indexing and conditional operations
Statistical functions — mean, median, standard deviation, variance, percentiles
Series and DataFrame objects for one-dimensional and two-dimensional data
Creating DataFrames from CSV, Excel, JSON files, and Python data structures
Reading data efficiently with read_csv, read_json, read_excel
Data exploration — .head(), .tail(), .info(), .describe() for quick insights
Data selection — loc, iloc, at, iat for precise label and integer-based access
Boolean indexing, conditional selection, and advanced filtering techniques
Handling missing data with .isna(), .dropna(), .fillna()
Removing duplicates, data type conversions, and string operations
Data transformation — .apply(), .map(), lambda functions
GroupBy operations and aggregations for feature engineering
Pivot tables, cross-tabulations, merging, joining, and concatenating DataFrames
Reshaping with stack, unstack, melt, pivot operations
Time series analysis and date/time handling
Figure and Axes object architecture for sophisticated plot composition
pyplot interface (MATLAB-like) vs object-oriented approach
Line plots showing trends over time
Scatter plots revealing relationships between variables with customisable markers
Bar charts comparing categories and histograms displaying data distributions
Pie charts illustrating proportional relationships
Customisation — colours, markers, line styles, labels, titles, legends
Ticks, grid lines, axis formatting, annotations, and text elements
Multiple subplots with grid layouts for comprehensive dashboards
Box plots and violin plots for visualising statistical distributions
High-level interface built on Matplotlib with sophisticated defaults
Distributions — histplot, kdeplot, boxplot, violinplot
Categorical plots — barplot, countplot, pointplot
Relational plots — scatterplot, lineplot, relplot
Heatmaps for correlation matrices and data tables with colour encoding
Clustermaps with hierarchical clustering for pattern discovery
Regression plots — regplot, lmplot, residplot for linear relationships
Pair plots and joint plots for multivariate relationships
Facet grids enabling multi-dimensional data exploration with subplot matrices
Professional colour palettes and themes for publication-ready visualisations
Plotly ecosystem — Plotly Express, Graph Objects, Dash
Plotly Express fundamentals for rapid visualization
Interactive features — hover, zoom, pan, selection
Line charts, scatter plots, and bar charts with interactivity
Box plots, violin plots, and histograms
3D scatter plots and surfaces
Geographic maps and choropleths
Animated visualisations for temporal data
Candlestick charts for financial data
Layout templates, customisation, subplots, and multi-plot dashboards
Exporting interactive plots to HTML for presentations and dashboards
05
Generative AI & Agentic AI
The production AI engineering destination. From the 70-year arc of AI history to
deploying production RAG-powered agents — the complete 2026 GenAI engineering stack: frontier
models, prompt engineering, multimodal AI, LLM APIs, vector databases, agentic frameworks, and the
Model Context Protocol. The Coding Agent project lives here.
Narrow AI — image classifiers, speech recognition — the pre-2022 era of
task-specific intelligence
Generative AI — LLMs, image/video/audio generation — the post-2022 era unleashed
by ChatGPT
Agentic AI — Plan / Reason / Act / Learn loops, tool use — the post-2024 era of
autonomous systems
2022 inflection point — ChatGPT launch enters mainstream consciousness and
professional workflows
2024 inflection point — AI systems begin planning, using tools, completing
multi-step tasks autonomously
What's coming 2026-2030 — increasingly capable reasoning models, deeper tool
integration
Multi-agent collaboration at scale and systems that learn continuously from
real-world feedback
LLM internals, the frontier model landscape, agent architecture, AI safety,
workstation setup
GPT-5.5 — The Autonomous Agent. Natively omnimodal. Terminal-Bench 2.0 leader at
82.7%
GPT-5.5 best for autonomous agents, computer use, terminal automation; 40% token
efficiency gain over GPT-5.4
Claude Opus 4.7 — The Precision Coder. Hybrid reasoning with extended thinking
mode
Claude Opus 4.7 — SWE-bench Pro leader at 64.3%, lowest hallucination rate at
36%, deepest native MCP support
Gemini 3.1 Pro — The Context Giant. Natively multimodal with 2M+ token context
window
Gemini 3.1 Pro best for high-volume batch jobs, multimodal media analysis,
cost-sensitive workloads
Open-source frontier — Llama 4 (Meta), DeepSeek, Mistral, Qwen
Intelligent Routing — the 2026 production pattern across multiple models
Microsoft Copilot Suite — Word, Excel, PowerPoint, Outlook, Teams integration
Copilot Studio — enterprise no-code agent building
Specialised tools — Perplexity (citation-grounded search), NotebookLM, ChatGPT
Codex
Anatomy of a great prompt — Context + Task + Examples + Format + Constraints
Core techniques — zero-shot, few-shot, Chain-of-Thought (CoT), ReAct,
Tree-of-Thought
Role-based prompting, output format control, negative prompting
System prompts — architectural difference, persistent persona design, guardrails,
extended thinking modes
Multimodal prompting — vision, image generation, audio, video
Gemini 3.1 Pro's native video and audio analysis capabilities
Hallucination mitigation — grounding, self-verification, citation prompting
Context engineering — token budgeting, lost-in-the-middle mitigation
Context Engineering — the 2026 frontier discipline that governs everything
entering the context window
Domain-specific prompt patterns — marketing, SEO, research, code generation,
customer service, legal, finance
Project — ship a 30+ prompt library on GitHub that recruiters will actually open
Using ChatGPT, Claude, and Gemini for everyday productivity
Document drafting, summarisation, and editing workflows
Research workflows with Perplexity and NotebookLM
Email, meeting, and task management with AI assistants
Microsoft Copilot in Word, Excel, PowerPoint, Outlook, Teams
AI for code — GitHub Copilot, Cursor, Claude Code, ChatGPT Codex
Building personal AI workflows that compound over weeks
Vision capabilities — image understanding, OCR, chart reading, screenshot
analysis
Image generation — Stable Diffusion, DALL-E, Midjourney, Imagen
Audio — speech-to-text (Whisper), text-to-speech, voice cloning
Video generation — Sora, Runway
Video analysis — Gemini 3.1 Pro native video
Native multimodal vs adapter-based multimodal — architectural differences
Practical applications — accessibility, content creation, data extraction from
images
Cost and latency considerations for multimodal workloads
Bias in AI systems — sources, detection, mitigation
Hallucination — why it happens, how to reduce it, how to verify
Privacy considerations — PII, data residency, on-prem vs cloud
Security — prompt injection, jailbreaks, data exfiltration risks
Regulatory landscape — EU AI Act, India DPDP Act, sector-specific rules
Ethical AI principles — fairness, transparency, accountability, human oversight
Building human-in-the-loop systems for high-stakes decisions
Responsible AI is not a separate workstream — it's woven into every design
decision
Streamlit — rapid AI app prototyping in pure Python
Building chat interfaces, dashboards, and demos
Session state, callbacks, and reactive patterns
FastAPI — production-grade Python API framework
Async endpoints for high-throughput AI workloads
Request validation with Pydantic
Authentication, rate limiting, observability
Deploying to Render, Railway, Vercel, or a VPS
Project — build and deploy a Streamlit + FastAPI chatbot that connects to an
OpenAI / Anthropic / Google LLM
LLM APIs in production — OpenAI, Anthropic, Google GenAI, DeepSeek Python SDKs
API patterns — completions, chat, streaming, function calling, structured outputs
Rate limits, retries, exponential backoff, cost tracking and observability
Function calling & structured outputs — the 2026 production pattern for reliable
JSON
Pydantic-validated structured outputs for type-safe AI
Embeddings — OpenAI text-embedding-3-large, Voyage, Cohere embedding models
Vector databases — ChromaDB (local/dev), Pinecone (managed), Qdrant
(open-source), pgvector (PostgreSQL)
Indexing strategies — HNSW, IVF
RAG pipeline — Chunk → Embed → Index → Retrieve → Augment → Generate
Chunking strategies — fixed-size, semantic, hierarchical
Hybrid search (BM25 + embeddings) and re-ranking with cross-encoders
Agentic RAG — self-improving retrieval where the agent decides if it has enough
information
Project — Production RAG App with ChromaDB or Pinecone, hybrid search,
re-ranking, deployed to a public URL
LangGraph 1.0 — complex stateful workflows, graph-based state machines, the
production default
Human-in-the-loop checkpoints and LangSmith observability
Claude Agent SDK — powers Claude Code, deepest MCP integration, extended thinking
CrewAI — role-based multi-agent crews, fastest prototyping, native MCP + A2A
support
Semantic Kernel / Microsoft Agent Framework — enterprise .NET stacks, GroupChat
debate pattern
Pydantic AI — type-safe Python, validation-first agent design
ReAct (Reasoning + Acting) pattern
Plan-and-Execute and Reflection loops
Multi-agent collaboration — supervisor, swarm, debate patterns
Production agents are 90% about state management, observability, and
human-in-the-loop — the LLM is the easy part
MCP — the open standard for connecting agents to tools, data, and systems
Proposed by Anthropic in late 2024, now stewarded by the Linux Foundation
200+ server implementations and 97M+ monthly SDK downloads
Adoption across Anthropic, OpenAI, Google, Microsoft, AWS, and 50+ partners
The transformative idea — write the integration once, every agent uses it
Build an MCP server exposing tools and resources with authentication
Connect LangGraph agents to multiple MCP servers via adapters
Use Claude Agent SDK's deepest native MCP integration
Connect CrewAI via crewai-tools[mcp] and build MCP-enhanced RAG pipelines
A2A Protocol — Google-led agent-to-agent communication standard with 50+ launch
partners
Three A2A state management levels — session-level, agent-level, task-level
(TaskStore)
Coding Agent Project — multi-agent system using LangGraph + Claude Agent SDK,
MCP servers exposing codebase, PostgreSQL, custom retrieval API, deployed with LangSmith
observability
06
Modern Python Framework FastAPI
The production API layer. FastAPI delivers 3x performance (matching Node.js and
Go), 100% type safety through Pydantic, and automatic Swagger UI / ReDoc documentation. Built on
Starlette and Pydantic, it is the production API layer for your AI agents and data services.
3x Performance — FastAPI matches Node.js and Go performance, significantly faster
than traditional Python frameworks
100% Type Safety — complete type hints enable automatic validation,
serialisation, and IDE support
Auto Documentation — Swagger UI and ReDoc generated automatically from code
annotations
Environment setup — create virtual environment and install FastAPI with Uvicorn
ASGI server
First Endpoint — define path operations using @app.get, @app.post, @app.put,
@app.delete
Launch development server with automatic reload for rapid iteration
ASGI (Asynchronous Server Gateway Interface) standard
Path operations, HTTP method decorators, request and response lifecycle
Built on Starlette (web framework) and Pydantic (data validation)
Path parameters with type hints — automatic conversion and validation
Query parameters with defaults — optional and required patterns
Pydantic models for request bodies — automatic validation, serialization, and
documentation
Type hints drive automatic validation
IDE support through type inference
Error messages auto-generated from type definitions
Pydantic-driven validation is what gives FastAPI its 100% type safety guarantee
SQLAlchemy ORM with dependency injection manages database sessions
Session management patterns for async and sync workloads
Connection pooling and lifecycle management
CRUD operations with full transactional support
Alembic migrations enable schema evolution
Database versioning and rollback strategies
Repository pattern separating database logic from business logic
Connecting to PostgreSQL databases configured in Section 3
Pydantic models mirroring SQLAlchemy models
ORM-to-API patterns for clean separation of concerns
APIRouter organises code into modular components
Versioned APIs — /v1, /v2 patterns
Feature-based vs layer-based organisation
Repository pattern separates database logic from API logic
Environment variables configure deployments across Dev, Staging, Prod
Configuration with Pydantic Settings
Dependency injection for testability
Modular folder structure for scalable applications
Service layer for business logic and Schema layer for Pydantic models
Router layer for endpoints and Test layer mirroring the application structure
Password Security — Bcrypt hashing protects user passwords with industry-standard
encryption
JWT Tokens — JSON Web Tokens with expiration enable stateless authentication
across requests
OAuth2 Flow — OAuth2PasswordBearer dependency verifies tokens and extracts
current user information
Protected routes — authentication dependencies secure endpoints
Role-based access control (RBAC) manages permissions
CORS configuration for cross-origin requests
Rate limiting to prevent abuse
Request validation, sanitisation, and secrets management with environment
variables and vaults
ASGI server deployment with Uvicorn + Gunicorn and Docker containerisation
Health checks, readiness probes, logging, monitoring, and observability
Deploying to Render, Railway, Fly.io, AWS, or GCP
Tools you'll master
40+ tools, one production project.
Real-time projects
You don't watch videos. You ship software.
Three full-production projects, each threaded through the entire curriculum. By the project, you've built the whole stack around them.
Hero project · weeks 3–12
LMS analytics platform
Ingest learner events, build transformation layers, and publish executive and academic dashboards with AI-generated insight summaries.
PySparkDatabricksPower
BILangGraphPostgreSQL
View project →
Enterprise · weeks 6–11
HRMS data pipeline
Build secure ETL workflows for employee, payroll, and performance datasets with governed semantic models and decision-ready KPIs.
MS FabricDelta LakePower BIUnity
Catalog
Real-time · weeks 8–12
CRM intelligence stream
Create near real-time customer analytics with streaming events, automated anomaly flags, and AI-assisted executive reporting.
Structured StreamingKQLPower
BILangChain
Project · weeks 11–12
Your AI Python Training agent in a real partner org.
Pick a real partner data problem. Deploy a production data pipeline and an AI agent that explains metrics, detects risks, and accelerates business decisions.
2026: 220+ deployed76% → placement offers
See project gallery →
Your instructor
Taught by engineers who shipped agentic AI to production.
AS
Aarav Sharma
Lead Instructor · Python Training & AI
React · FastAPI · PyTorch · LangChain
"A 2026 full-stack engineer doesn't stop at React + an API. They train the model,
deploy it behind FastAPI, wrap it in an agent, and ship the whole thing to a real org. That's what we build,
every cohort."
10 yrs
FULL STACK
2,400+
LEARNERS
4.9 /5
RATING
Aarav started as a React engineer at an Indian unicorn before leading platform teams across three continents. He's shipped React + FastAPI products for a healthcare network with 80M users, trained NLP classifiers in production for a top-3 bank, and — most recently — deployed the first LangGraph agent into a Fortune-500 insurer's claims pipeline.
His classes get two things other programs don't give you: a real engineer who still ships code, and a curriculum rewritten every quarter to match what hiring managers actually ask about.
FAQ
Questions we actually get — answered honestly.
If the answer you need isn't here, book a 20-minute advisor call. No-slides, no-pitch — just your questions.
No. About 40% of our Python Training class comes from non-CS backgrounds — mechanical,
electrical, and commerce. The first phase is foundations by design. What you need: consistency and around
12–15 hours/week.
Plan for 12–15 hours: 2 live classes × 2 hours, 1 lab × 3 hours, and roughly 5 hours of
asynchronous project work. Weekends are optional office hours with the TA team.
Yes. Every student gets a dedicated placement advisor from week 8 onwards — not a helpdesk.
They review your resume, redo your LinkedIn, mock-interview you, and make direct warm introductions to our
1,000+ hiring partners. We track individual outcomes, not class averages.
Full refund within 7 days of class start, no questions. Pro-rata refund through week 4 if
the program isn't working for you. We'd rather refund than have an unhappy alum.
You actually build. Sections 6 (ML), 7 (DL/NLP), and 8 (Generative + Agentic AI) are
hands-on — you'll train classifiers, build a RAG pipeline, ship a LangGraph workflow, and deploy your
project agent into a real partner org. Nothing in the AI track is theory-only.
You get the Agent-Ready 2026 credential, graded on a 1–5 band with a public verification
URL. It's co-branded with our partner ecosystem (Salesforce Partner + Python Training ), and it names the
specific
project artifact you deployed. Recruiters can verify in 10 seconds.
All three. On-campus at our Hyderabad flagship; online classes on IST and PST; weekend
classes for working professionals. Every format ships the same three projects and the same project.
We'd rather pause your class than push you through. You can freeze your seat for up to 90
days and rejoin the next class without paying again. TAs run catch-up sessions every Saturday for anyone
more than one week behind.
Class 014 starts 1 Jun 2026.
40 seats. 12 already claimed.
Book a 20-minute advisor call. We'll walk through the curriculum, match it to your current role, and show you two real projects from class 022.
