🔥 Enrollment Now Open — Applied Conformal Prediction

This is the most practical, zero-hype course on Conformal Prediction available—designed to move you from curiosity to confident deployment.

If you're tired of vague uncertainty estimates and black-box trust issues, you're in the right place.

We cap enrollment to create a tight, high-value learning environment—not a mass-market MOOC.

🧠 Built for Real-World Machine Learning Practitioners

✅ Struggling to quantify uncertainty in your models with confidence?

✅ Need reliable predictions that stand up in production—not just in notebooks?

✅ Tired of “explainability” that’s vague, unconvincing, or not actionable?

This course isn’t about academic exploration.

It’s about building trustworthy, interpretable, and calibrated AI systems—at scale.

✅ What You’ll Learn to Do

💎 Apply Conformal Prediction to any ML pipeline—classification, regression, time series, Computer Vision and NLP

💎 Quantify uncertainty rigorously, using proven, distribution-free techniques

💎 Deliver reliable prediction intervals with real-world performance guarantees

💎 Communicate risk clearly to non-technical stakeholders

💎 Use Conformal Prediction in production with modern ML and Conformal Prediction tools.

Every module is hands-on, practical, and tested in real industry use cases.

eLarn why Conformal Prediction works, and how to use it to deliver measurable ROI, unlock new ML capabilities, and secure your competitive edge for the future.

👨‍🏫 Meet Your Instructor: Dr. Valeriy Manokhin

Dr. Manokhin is a recognized authority on Conformal Prediction—trained under its original creator, Professor Vladimir Vovk.

He’s the author of the bestselling Practical Guide to Applied Conformal Prediction, creator of the Awesome Conformal Prediction GitHub repo (3,300+ stars), and an active researcher published in top machine learning journals.

✅ Built AI systems for Fortune 500s and global consultancies

✅ Published peer-reviewed work on uncertainty quantification

✅ Advised startups and financial firms on deploying trustworthy ML

His approach blends deep academic insight with real-world deployment experience—you’ll get both the theory andthe practice that matter.

🌍 Trusted by ML Professionals at:

Amazon, Apple, Google, Meta, Nike, BlackRock, Dell, Target, Ericsson, ABN Amro, BBVA, Cisco, State Farm, Yum!, OKX, Squarepoint Capital, Ralph Lauren, Volvo, Monzo, Globo, CBC/Radio-Canada, AIA Singapore, Align Technology, Fetch, Fractal Analytics, CloudWalk, Markel, Vrify, Mantel Group, Crateflow, Spice AI, Maclear Data Solutions, Mercado Libre—and many more.

Plus researchers and professionals from world-class institutions including:

KTH, DTU, USC, Universidad Carlos III de Madrid, Politecnico di Milano, Universitat Politècnica de Catalunya, Utah State University, Princess Nourah University, and others.

Participants include:

✅ Executives and VPs of Engineering

✅ Principal and Lead Data Scientists

✅ Quant Researchers and AI Engineers

✅ Independent Consultants and Industry Trainers

✅ University Professors and PhD Students

This is a course for professionals who lead, teach, and build real-world machine learning systems that must be trusted, interpretable, and production-ready.

❌ What This Course Is Not

🚫 Not a shallow overview of uncertainty

🚫 Not just another Python tutorial

🚫 Not a "trust the math" black box

This is a strategic, rigorous, and actionable program for professionals who care about model trust, interpretability, and risk-aware AI.

✅ Final Call

If you're building models that affect real-world decisions—finance, healthcare, logistics, energy, or tech—uncertainty isn't optional. It's mission-critical.



Learn Conformal Prediction the right way—so you can deploy it with confidence.

📅 Enrollment is open now

⚠️ Limited seats per cohort

💥 Price may increase without notice

👉 Secure your seat today and build machine learning systems you—and your stakeholders—can truly trust.