From Fundamentals to Real‑World GenAI and ML Systems
I work hands-on, lead Data Science and GenAI teams, sit in hiring panels daily, build projects for top companies and teach with the clarity that comes from real-world deployment experience.
Imperial College London & 
Indian Institute of Management Bangalore Delivered Production AI Solutions For:
Rolls-Royce
Unilever
- Rolls-Royce
- Unilever
Deployed predictive maintenance systems for aircraft engines.
Reduced Understock in Latin America.
Forecasted Demand across North America.
Generating quizzes for sales representatives in Japan.
I Build AI Systems Every Day: Teaching Is How I Give Back.
Explore my technical breakdowns to see my teaching style before you commit.
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Live Projects: See What You'll Build
These are real applications I deployed recently. By the end of this course, you'll build and deploy similar production-ready systems.
🤖 Agentic Resume Builder
AI agent that tailors resumes to specific job descriptions. Demonstrates multi-agent orchestration and production deployment.
Tech Stack: CrewAI • FastAPI • Render
Live Demo →🌍 PM2.5 Forecasting System
End-to-end ML deployment with automated daily predictions, scheduled workflows, and database integration.
Tech Stack: ARIMA • Deep Learning • Flask • GitHub Actions • Neon DB • Render
Live Demo →📚 Gen AI RAG Chatbot
Production-ready RAG chatbot for article analysis. This is exactly what every student will deploy by course completion.
Tech Stack: LangChain • OpenAI • Vector DB • Streamlit
Live Demo →Curriculum Roadmap
Phase 1: Foundations & Stats (Weeks 1-3)
Python Essentials • Hypothesis Testing (Z-tests, ANOVA) • Exploratory Data Analysis with Real Datasets
Phase 2: Advanced ML & Deep Learning (Weeks 4-7)
Regression & Classification • XGBoost/Ensembles • Model Tuning & Validation • Multi Models
Phase 3: GenAI & Production (Weeks 8-11)
LLMs (BERT/GPT) • Prompt Engineering • RAG with LangChain • Agentic Frameworks • Deployment (Docker, Streamlit, Cloud)
Phase 4: Optional Section (Weeks 11-16)
Other topics • Clustering • Optimisation • Time Series • GitHub Copilot
The Production Advantage: What Sets This Course Apart?
Industry first
This isn’t just about modelling: it’s about the entire journey: framing the problem, planning the approach, executing the solution, and delivering it in the real world.
Real Outcomes
Every student builds a personalized ML case study and a live GenAI Chatbot you can showcase in your portfolio.
Career Prep
Resume optimization and interview prep based on real hiring experience at Fortune 500 companies: get hired, not just educated.
Sri Harsha Achyuthuni
Data Science Manager at Deloitte
Harsha is a data science professional with a track record of delivering high-impact AI and ML solutions across Fortune 500 companies.
Education: MSc in Business Analytics from Imperial College London • Executive Education from IIM Bangalore
Expertise: Predictive maintenance, demand forecasting, optimization engines, classification, NLP, supply chain analytics, and generative AI applications.
Industry: Consulting, manufacturing, consumer goods, supply chain, pharmaceuticals, healthcare, insurance, retail, defence, and aerospace sectors.
GitHub Activity
Professional Network
Ready to Start Your Journey?
Explore more about my work and get in touch.