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Cohort 014 · Physical AI & AI Agents · Enrolling Now

Physical AI
+ AI Agents

Master end-to-end Physical AI and AI Agents with real-world, job-ready implementation skills. Build foundations in Python and SQL, ship pipelines with PySpark and Databricks, scale on Microsoft Fabric, and integrate Generative + Agentic AI into production data workflows.

3mo
duration
30+
modules
4.7/5
cohort rating
100k+
enrolled
Where our Physical AI alumni work
MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL MicrosoftAmazonSalesforceAI EngineerDeloitteInfosysAccentureTCSWiproCapgeminiCognizantHCL
What you leave with

Four things every Physical AI grad walks away with.

Most programs stop at tools. Ours makes you ship pipelines, platforms, and AI-powered data products that hiring teams can verify.

01
Agent-Ready skills
Build, deploy, and monitor AI agents that run production workflows — not chatbot toys.
02
A shipped capstone
A live React + FastAPI + LangGraph app on Kubernetes, monitored, observable, public URL.
03
Verifiable credential
2026 Agent-Ready rubric, graded 1–5 with a public verification URL recruiters can check.
04
Direct placement pipeline
GitHub + LinkedIn rewrite, resume rebuild, and warm intros to our 1,000+ hiring partners.
3 months, four phases

From "loads CSVs" to ships AI-native data pipelines.

Weeks 1–3 build Python and SQL depth. Weeks 4–7 cover Power BI and data storytelling. Weeks 8–10 move into PySpark, Databricks, and Fabric. Weeks 11–12 ship Generative + Agentic AI data agents.

WEEKS 1–3 · FOUNDATIONS

Python + SQL for Physical AI

  • Python data structures, iterators, OOP
  • PostgreSQL querying, joins, windows, CTEs
  • Database design, indexing, optimization
  • Data file formats and transformation patterns
YOU SHIPA Python + SQL ingestion and transformation workflow over production-like datasets.
WEEKS 4–7 · ANALYTICS

Power BI and business intelligence

  • Power Query and source integrations
  • Star schema modeling and DAX measures
  • Advanced visuals, storytelling, KPI dashboards
  • Publishing, sharing, governance, refresh
YOU SHIPA complete Power BI reporting suite consumed by business teams and leaders.
WEEKS 8–10 · DATA PLATFORM

PySpark, Databricks, and Microsoft Fabric

  • Spark DataFrames, joins, windows, optimization
  • Databricks workflows, Delta Lake, Unity Catalog
  • Fabric OneLake, Lakehouse, Warehouse, RTI
  • Streaming, orchestration, and governance
YOU SHIPAn enterprise-grade ELT platform with scheduled jobs, observability, and governed data products.
WEEKS 11–12 · GENERATIVE + AGENTIC AI

Deploy AI agents that automate analytics, retrieval, and reporting across your data platform.

Use LLM APIs, LangChain, RAG, and LangGraph workflows with persistence and HITL. Add MCP tool access and enterprise guardrails. Your capstone connects pipelines, dashboards, and AI agents into a single production-ready data intelligence system.

Partner orgs (2026)48
Capstones deployed280+
→ Placement offers82%
Course curriculum

Seven sections. 65+ modules. The AI-native Physical AI stack.

Jump to any section on the left. Click a module to see topics, hands-on lab, and key technologies.

01

Python for AI

How modern apps work, how teams ship them with Agile, where compute & cloud fit, and how AI plugs into the 2026 stack.
10 MODULES
WEEK 1
1. What is an Application? 2. Types of Applications 3. Web Application Fundamentals 4. Web Technologies: (List key technologies and their roles) Frontend: HTML, CSS, JavaScript, React Backend: Python, Java, Node.js Databases: SQL (MySQL, PostgreSQL), NoSQL (MongoDB). 5. Software Development Life Cycle (SDLC) Phases: Planning, Analysis, Design, Implementation (Coding), Testing, Deployment, Maintenance. 6. Application Development Methodologies Agile: Core principles, Scrum, Kanban Waterfall
1. What is Data 2. Types of Data 3. Data Storage 4. Data Analysis 5. Data Engineering 6. Data Science
1. The Importance of Computing Power 2. Key Computing Technologies: CPU (Central Processing Unit) GPU (Graphics Processing Unit) 3. Cloud Computing: What is the Cloud? Cloud Service Models: IaaS (Infrastructure as a Service) PaaS (Platform as a Service) SaaS (Software as a Service)
1. What is Artificial Intelligence (AI)? 2. How AI Works? 3. Key Concepts: Machine Learning (ML) Deep Learning (DL) 4. Generative AI: What is Generative AI? Examples: Large Language Models (LLMs), image generation models. 5. AI in Everyday Learning
1. Customer Relationship Management (CRM) 2. Human Resource Management Systems (HRMS) 3. Retail & E-Commerce 4. Healthcare
1. NumPy Arrays and Operations 2. Array Indexing and Slicing 3. Mathematical and Statistical Functions
1. DataFrames and Series 2. Data Cleaning and Transformation 3. Aggregations and GroupBy
1. Matplotlib Basics 2. Seaborn for Statistical Visualization
1. Scikit-learn, TensorFlow, PyTorch Overview 2. Environment Setup for AI Development
1. End-to-End Python Data Pipeline 2. Capstone Project
02

Data Engineering & Fabric

Modern React with hooks, Redux Toolkit and routing, paired with PostgreSQL fundamentals through query optimization.
10 MODULES
WEEKS 2–4
Overview of Analytics and Power BI Tools Suite Career Opportunities and Job Roles in Power BI Power BI Data Analyst (PL 300) Certification Overview Introduction to AI Visuals and Features in Power BI
Understanding the Power BI Ecosystem and Architecture Data Sources and Types for Power BI Reporting Power BI Design Tools and Desktop Tool Installation Exploring Power BI Desktop Interface: Data View, Report View, and Canvas
Visual Interaction Techniques in Reports Using Slicers for Dynamic Report Filtering Managing Report Pages and Visual Sync Limitations
Implementing Grouping and Binning in Reports Creating and Utilizing Hierarchies for Drill-Down Reports
Introduction to Power Query M Language Basic Data Transformations in Power Query Understanding Query Duplication and Grouping
Overview of Power BI Cloud Components and App Workspaces Creating and Managing Reports and Dashboards in Power BI Cloud Sharing, Subscribing, and Exporting Reports in Power BI Cloud
Understanding the Importance of DAX in Power BI Learning Basic DAX Syntax, Data Types, and Contexts Simple Measures and Calculations with DAX
Building data pipelines in Fabric Data flow design and execution
Workflow automation Monitoring and alerting
Data quality frameworks Governance and compliance
03

Data Science (Predictive AI)

Python from fundamentals through OOP, then FastAPI — async APIs with Pydantic validation, SQLAlchemy, and JWT auth.
15 MODULES
WEEKS 5–8
Accessing Big Data Sources and Azure Databases Advanced Filtering Techniques and Utilizing Bookmarks Implementing Various Chart Types and Map Visuals
Deep Dive into Advanced Data Cleaning and Preparation Techniques Implementing Parameter Queries for Dynamic Data Loads Creating and Managing Parameters in Power Query
Configuring and Managing Gateways for Data Refresh Utilizing Workbooks and Excel Online with Power BI Cloud Creating and Managing Power BI Apps
Implementing Quick Measures and Advanced Calculations Data Modeling and Relationship Management in DAX Mastering Variables and Dynamic Expressions in DAX
Advanced DAX Functions for Time Intelligence Implementing Row Level Security (RLS) with DAX Utilizing DAX for Custom Analytics and Reporting
Configuring Power BI Report Server Understanding Power BI Administration and AI Features Managing Security and Administration in Power BI
Cross-validation, metrics, hyperparameter tuning
Forecasting, ARIMA, Prophet
Model serving, APIs, production best practices
04

Generative AI

Production Physical AI : Power BI for analytics, then Microsoft Fabric — OneLake, Lakehouse medallion architecture, Spark, real-time intelligence, and Copilot.
25 MODULES
WEEKS 9–14
Topics: Introduction to Excel: Interface, Basic Operations, and Managing Worksheets Fundamental Data Operations: Sorting, Filtering, and Conditional Formatting Basic Formulas and Functions: Sum, Average, Logical Functions (IF, AND, OR), and Text Functions (LEFT, RIGHT, CONCATENATE)
Topics: Advanced Data Management: Data Validation, Advanced Filtering, and Named Ranges Creating and Managing Tables for Efficient Data Analysis Introduction to Data Visualization: Creating and Customizing Charts (Bar, Line, Pie), and Using Sparklines
Topics: Comprehensive Guide to PivotTables: Creating, Customizing, Slicers, and Timelines Basic to Advanced PivotTable Techniques: Grouping Data, Calculated Fields Data Cleanup Techniques: Removing Duplicates, Text to Columns, Flash Fill
Topics: Mastering Lookup Functions: VLOOKUP, HLOOKUP, XLOOKUP Introduction to Power Query for Data Transformation and Cleaning Power Pivot and DAX Basics: Creating Data Models, Introduction to DAX Formulas for Data Analysis
Topics: Automating Tasks with Macros and an Introduction to VBA for Custom Functions Advanced Chart Techniques and Creating Interactive Dashboards Workbook Protection, Sharing Workbooks for Collaboration, Documenting and Auditing Workbooks
Image, audio, and video generation models
API integration
Building production GenAI applications
Bias, safety, ethical considerations
End-to-end GenAI project
05

Agentic AI

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.
5 MODULES
WEEK 15
Topics: Understanding Table Relationships: Primary keys, foreign keys, and the importance of relationships in databases. Join Operations: `INNER JOIN`, `LEFT JOIN`, `RIGHT JOIN`, and `FULL JOIN`. Subqueries and Nested Queries: Using subqueries in the `SELECT`, `FROM`, and `WHERE` clauses. Aggregating Data: Using `GROUP BY` and aggregate functions (`COUNT`, `SUM`, `AVG`, `MIN`, `MAX`).
Topics: Data Manipulation Commands: `INSERT`, `UPDATE`, `DELETE`. Managing Tables: Creating and altering tables (`CREATE TABLE`, `ALTER TABLE`, `DROP TABLE`). Advanced Filtering Techniques: Using `LIKE`, `IN`, `BETWEEN`, and wildcard characters. Working with Dates and Times: Understanding and manipulating date and time data.
Advanced SQL Functions: String functions, mathematical functions, and date functions. Window Functions: Overviews of `ROW_NUMBER`, `RANK`, `DENSE_RANK`, `LEAD`, `LAG`, and their applications. Query Performance Optimization: Indexes, query planning, and execution paths. Common Table Expressions (CTEs): Writing cleaner and more readable queries with `WITH` clause.
Topics: Analytical SQL for Reporting: Building complex queries to answer analytical questions. Pivoting Data: Transforming rows to columns (`PIVOT`) and columns to rows (`UNPIVOT`). Data Warehousing Concepts: Introduction to data warehousing practices and how they apply to SQL querying. Integrating SQL with Data Analysis Tools: Connecting SQL databases with tools like Excel, Power BI, and Python for deeper data
Agent frameworks and orchestration
Multi-agent collaboration patterns
Persistence and context handling
Self-directed agent execution
Testing and safety guardrails
Deployment and scaling
06

Physical AI (Robotics)

The mathematical backbone behind every ML and DL model: linear algebra, probability, distributions, hypothesis testing, and applied statistics for ML.
5 MODULES
WEEK 15
### Topics: 1. Introduction to Python Overview of Python's history, key features, and comparison with other languages. Setting up the Python environment, writing your first program. 2. Core Programming Concepts Variables, data types, conditional statements, loops, control flow. Introduction to strings, string manipulation, and basic functions
Topics: 1. Deep Dive into Collections Understanding lists, tuples, dictionaries, sets, and frozen sets. Functions, methods, and comprehensions for collections. 2. Functional Programming in Python Exploring function arguments, anonymous functions, and special functions (map, reduce, filter). 3. Object-Oriented Programming (OOP) Classes, objects, constructors, destructors, inheritance, polymorphism. Encapsulation, data hiding, magic methods, and operator overloading.
Topics: 1. Mastering Exception Handling Exception handling mechanisms, try & finally clauses, user-defined exceptions. 2. File Handling Essentials Basics of file operations, handling Excel and CSV files. 3. Database Programming Introduction to database connections and operations with MySQL
Topics: 1. Getting Started with Flask Setting up Flask, creating simple applications, routing, and middleware. 2. Exploring Django Introduction to Django, MVC model, views, URL mapping.
Topics: 1. Automation and Scripting Enhancing file handling, database automation, and web scraping with BeautifulSoup. 2. GUI Development with TKinter Basics of TKinter for developing desktop applications. 3. Version Control with Git Managing projects with Git, understanding repository management, commits, merging, and basic Git commands.
Motion planning, SLAM, obstacle avoidance
Reinforcement learning, control systems
Natural language, gesture, collaborative robotics
On-device ML, edge computing
End-to-end robotics project
Tools you'll master

40+ tools, one production capstone.

Not a shallow tour. You'll use every one of these in at least one graded exercise.

R
React 18
RT
Redux Toolkit
TS
TypeScript
V
Vite
Nd
Node.js
Py
Python
FA
FastAPI
SA
SQLAlchemy
Pg
PostgreSQL
M
MongoDB
PB
Power BI
MF
MS Fabric
Np
NumPy
Pd
Pandas
Sk
scikit-learn
TF
TensorFlow
PT
PyTorch
HF
Hugging Face
SM
spaCy
OAI
OpenAI
LC
LangChain
LG
LangGraph
LS
LangSmith
MC
MCP
VD
Vector DBs
D
Docker
K
Kubernetes
G
Git
GH
GitHub
aws
AWS
Az
Azure
C
Cursor AI
Real-time projects

You don't watch videos. You ship software.

Three full-production projects, each threaded through the entire curriculum. By the capstone, 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
Capstone · weeks 11–12

Your AI Physical AI 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 capstone gallery →
Your instructor

Taught by engineers who shipped agentic AI to production.

Not a career trainer. A practitioner who still ships code.

AS
Aarav Sharma
Lead Instructor · Physical AI & 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 cohorts 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 Physical AI cohort 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 cohort averages.
Full refund within 7 days of cohort 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 capstone 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 + Physical AI ), and it names the specific capstone artifact you deployed. Recruiters can verify in 10 seconds.
All three. On-campus at our Hyderabad flagship; online cohorts on IST and PST; weekend cohorts for working professionals. Every format ships the same three projects and the same capstone.
We'd rather pause your cohort than push you through. You can freeze your seat for up to 90 days and rejoin the next cohort without paying again. TAs run catch-up sessions every Saturday for anyone more than one week behind.

Cohort 014 starts 14 May 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 capstones from cohort 022.

₹89,000
₹1,20,000
25% off · EARLY BIRD
3 MONTHS · STARTS 14 MAY · 40 SEATS · 12 CLAIMED

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