The Trinity Bundle: Master Tabular, Time Series & Uncertainty (Standard Edition)
This bundle contains...
Master Tabular Modeling, Time Series Forecasting & Uncertainty — in One Practical Toolkit
Accelerate your machine learning skills with this curated bundle of hands-on, production-grade resources. The Trinity Bundle brings together three essential domains for applied ML: tabular modeling, forecasting, and uncertainty estimation.
📘 Mastering CatBoost
Dominate tabular data with CatBoost — one of the most powerful yet underrated tools in machine learning. Learn advanced modeling techniques, categorical encoding, missing data handling, model interpretation, and more.
📗 Mastering Modern Time Series Forecasting (Standard Edition)
Build state-of-the-art forecasting pipelines with practical recipes, cutting-edge methods, and real-world case studies. Covers everything from classical models to feature-rich machine learning pipelines and deployment-ready solutions.
📙 Advanced Conformal Prediction
Bring trust and uncertainty estimation into your models with the latest conformal prediction techniques. Learn how to quantify prediction confidence in regression, classification, and time series — with intuitive theory and practical code.
💡 Why This Bundle?
Together, these three books form a complete ML stack:
- 🔹 CatBoost for structured/tabular data
- 🔹 Time Series Forecasting for temporal insights
- 🔹 Conformal Prediction for model trust and reliability
💰 Bundle Price (Standard Edition): $90
Save vs buying individually, and lock in early-access pricing before full releases increase to $80–150+ per title.
Unlock practical mastery in three essential pillars of modern machine learning: ✅ What You Get: 📘 Mastering CatBoost Build powerful, interpretable tabular models with one of ML’s most underrated tools. 📗 Mastering Modern Time Series Forecasting (Standard or Pro) Learn to forecast with state-of-the-art techniques, practical code, and hands-on use cases. 📙 Advanced Conformal Prediction Add trust to your models with cutting-edge uncertainty quantification techniques.