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12 Machine Learning: Naive Bayes

Machine Learning Graduate Course, Professor Michael J. Pyrcz Lecture Summary: Lecture on the Naive Bayes Classifier, a very powerful, flexible prediction method based on fundamental Bayesian statistics. Bayesian Statistics + Machine Learning = Awesome. Here is a list of all my predictive machine learning lectures: 0. Machine Learning Concepts -    • 06 Machine Learning: Introduction to Machi...   1. Linear Regression -    • 08b Machine Learning: Principal Component ...   2. Ridge Regression -    • 08b Machine Learning: Principal Component ...   3. LASSO Regression -    • 10b Machine Learning: LASSO Regression   4. Optimization Basics -    • 10b Machine Learning: LASSO Regression   5. Polynomial Regression -    • 10c Machine Learning: Polynomial Regression   6. Training and Testing -    • 10d Machine Learning: Training and Testing   7. Model Goodness Metrics -    • 10e Machine Learning: Metrics for Tuning H...   8. Model Cross Validation -    • 10f Machine Learning: Cross Validation Con...   9. K-nearest Neighbours -    • 11 Machine Learning: k-Nearest Neighbors   10. Computational Complexity -    • 11b Machine Learning: Computational Comple...   11. K-nearest Neighbours Considerations -    • 11b Machine Learning: Computational Comple...   12. Bayesian Linear Regression -    • 11d Machine Learning: Bayesian Linear Regr...   13. Markov Chain Monte Carlo -    • 11e Machine Learning: Markov Chain Monte C...   14. Bayesian Linear Regression Example -    • 11f Machine Learning: Bayesian Regression ...   15. Naive Bayes -    • 12 Machine Learning: Naive Bayes   16. Decision Tree -    • 14 Machine Learning: Decision Tree   17. Random Forest -    • 15 Machine Learning: Random Forest   18. Gradient Boosting -    • 15b Machine Learning: Gradient Boosting   19. Support Vector Machines -    • 16 Machine Learning: Support Vector Machines   Note, I have excluded my deep learning predictive modeling lectures for brevity. Free, Online Course e-book Chapter: Naive Bayes - https://geostatsguy.github.io/Machine... Theory and well-documented workflows linked to the lectures and interactive Python dashboards. Course Summary: Welcome to Subsurface Machine Learning, a graduate-level course I teach at The University of Texas at Austin. While the course is officially titled “Subsurface Machine Learning” to highlight its focus on applications in subsurface modeling, the material is broadly applicable and designed to equip you with foundational and advanced machine learning skills relevant across diverse fields. The course begins with core concepts in spatial and subsurface modeling, grounded in geoscience and engineering principles, and builds a solid foundation in probability, statistics, and feature engineering and selection. From there, we explore inferential and predictive machine learning techniques, advancing all the way through to cutting-edge deep learning methods. Throughout the course, I strive to make complex topics accessible, providing a clear pathway to mastering machine learning for real-world challenges and empowering you to confidently navigate and harness the power of the ongoing digital revolution. Course Resources: Course e-book: https://geostatsguy.github.io/Machine... Course YouTube Playlist:    • Machine Learning   My Shared Educational Content: YouTube:    / geostatsguylectures   GitHub: https://github.com/GeostatsGuy Twitter: https://x.com/GeostatsGuy I share all of my university educational content to support students and working professionals interested to learn data analytics, geostatistics, and machine learning. More About the Author: https://michaelpyrcz.com Find out more about my graduate students, my research consortium, etc. I am happy to discuss research collaboration and short courses. I hope that you find my educational course content helpful on your data science journey, Professor Michael J. Pyrcz Cockrell School of Engineering Jackson School of Geosciences The University of Texas at Austin #dataanalytics #datascience #geostatistics #machinelearning