Practical AI & Data Science Learning
Turn Fuzzy Knowledge Into Job-Ready Skills.
Dense, practical and project-driven AI & Data Science learning designed to help you understand concepts, build real systems and develop skills you can actually use.
- Project-Based Learning
- Production-Focused
- Practitioner-Led
- Free Learning Resources
Learn it. Build it. Ship it.
Learn
Understand concepts properly through structured lessons, notes and guided learning.
Build
Build real AI and Data Science projects rather than stopping at theory.
Ship
Learn how applications move from notebooks into usable products and production workflows.
Courses Built Around Things You Can Actually Build
Practical learning paths designed around implementation, projects and real-world AI systems.
Don't Just Watch. Build.
Every course and workshop is built around production-shaped projects — not toy demos.
Production AI Agent
A tool-using AI agent taken from prototype to a deployed, monitored service — the same path students follow in Ship Your First AI Agent.
- Tools
- Python, LLM APIs, Vector store, FastAPI
- Architecture
- Tool-calling agent loop with short-term memory and a retrieval layer for grounded answers.
- Deployment
- Containerized service with request logging and basic evaluation on every release.
- Designing an agent loop that stays reliable across turns
- Wiring tool use into an LLM application, not just prompting
- What changes between a notebook demo and a deployed service
Retrieval-Augmented Knowledge Base
A document Q&A system built around retrieval quality and evaluation, not just similarity search — the focus of RAG in Production.
- Tools
- Python, Embeddings, Vector database, LLM APIs
- Architecture
- Chunking and retrieval pipeline feeding a grounded generation step, with a separate evaluation harness.
- Deployment
- API-backed service with retrieval metrics tracked alongside answer quality.
- Chunking and retrieval strategies that hold up on real documents
- Evaluating a RAG system instead of eyeballing outputs
- Where retrieval pipelines quietly fail in production
Applied Forecasting Pipeline
An end-to-end data science pipeline — from a messy real-world dataset to a model with a written stakeholder summary.
- Tools
- Python, Pandas, Scikit-learn
- Architecture
- Feature pipeline feeding a trained forecasting model with a validation and reporting step.
- Deployment
- Scheduled batch pipeline producing a refreshed forecast and summary report.
- Framing an ambiguous business question as a data problem
- Building a pipeline that survives contact with real data
- Communicating model results to a non-technical stakeholder
Useful Resources. Free.
Learn From People Who Actually Build.
Practitioner-led learning from instructors with strong technical, academic and industry backgrounds.
More instructors joining soon
We’re expanding the teaching team with more practitioners.
A clear path from foundations to shipped work.
Learn Foundations
Build a real understanding of the core concepts, not just surface familiarity.
Build Guided Projects
Apply what you learned with structure, feedback and support.
Build Independently
Take on projects of your own with the judgment to make design decisions.
Ship Real Applications
Move from notebook to deployed, usable system.
For business
Build AI Capability Across Your Team.
Corporate workshops, structured training, and consulting for teams that want to use AI with real judgment — not just tools.
- Corporate AI Workshops
- AI & Data Science Training
- AI Consulting
- Custom AI Solutions
Practitioner-Led
Hands-On, Not Just Theory
Tailored To Your Team
Real Judgment, Not Just Tools
Learning Is Better When You Don’t Do It Alone.
- Ask questions.
- Share projects.
- Learn from other builders.
- Get practical guidance.
Feedback From People Who Learned Here.
“The PDF course is honestly a goldmine. The questions are exactly the kind of questions product-based companies ask. It also covers advanced topics instead of stopping at the basics.”
Anjali T.
Data Analyst Aspirant
“Super helpful for interview prep. The questions are relevant and the solutions don't just explain what to do — they also help you understand why.”
Rahul S.
ML Engineer Trainee
“I could clearly see these were real interview questions. It even had questions similar to ones I was asked during my Swiggy interview.”
Priya M.
Career Switcher to Data Science
A practical first step
Stop Collecting Tutorials. Start Building.
Build practical AI and Data Science skills through structured learning, real projects and resources designed for implementation.