Overview

Bayesian Statistics and Machine Learning with Python A Hands-On Guide to Probabilistic Programming, Statistical Modeling, and Data Science Using PyMC, Stan, and Scikit-Learn Take your data skills to the next level! This hands-on guide makes Bayesian statistics and machine learning approachable, practical, and immediately useful. Learn to think probabilistically, model uncertainty, and make smarter predictions with Python. Inside, you'll discover how to: Harness probabilistic programming with PyMC and Stan to build powerful, flexible models. Apply advanced Bayesian techniques like hierarchical models, MCMC, and Bayesian regression to real-world problems. Boost machine learning workflows with uncertainty-aware classifiers, Bayesian neural networks, and Scikit-Learn integration. Handle messy data, validate models, and communicate results with confidence to stakeholders. Deploy robust, reproducible models that thrive in real-world applications. With clear explanations, step-by-step Python examples, and real-world case studies, this book is perfect for beginners and experienced practitioners alike. By the end, you'll not only understand Bayesian statistics-you'll know how to put it into action for smarter, data-driven decision-making. Whether your goal is research, AI development, or data science excellence, this book gives you the tools to model uncertainty, enhance predictions, and solve complex problems with confidence. Take control of your data. Master Bayesian thinking. Transform your approach to machine learning today

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