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An Introduction to Generative Models - Naive Bayes for Binary Features
Learn the fundamentals of Naive Bayes, from its conditional independence assumption to the maximum likelihood estimation (MLE) of parameters, using a binary feature example.
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Generalized Linear Models Explained - Leveraging MLE for Regression and Classification
Explore how Maximum Likelihood Estimation (MLE) forms the backbone of generalized linear models, enabling robust solutions for regression, classification, and beyond.
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Unveiling Probabilistic Modeling
Explore the fundamentals of probabilistic modeling and how it enhances our understanding of linear regression, from parameter estimation to error distribution.
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SVM Solution in the Span of the Data
This blog explores how the span property simplifies optimization in SVM and ridge regression, introduces the Representer Theorem, and highlights the computational benefits of kernelization.
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Understanding the Kernel Trick
A step-by-step exploration of kernel methods, unraveling their role in enabling powerful nonlinear modeling through the elegance of the kernel trick.