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Online Learning in ML - A Beginner’s Guide to Adaptive Learning
Learn how online learning transforms machine learning by handling dynamic, real-time data and adversarial scenarios. Explore its advantages, real-world applications, and key concepts like regret minimization and the Halving Algorithm in this beginner-friendly guide to adaptive AI.
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Multivariate Gaussian Distribution and Naive Bayes
Dive into the multivariate Gaussian distribution, its role in probabilistic modeling, and how it powers Naive Bayes classifiers with practical insights and mathematical intuition.
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Gaussian Naive Bayes - A Natural Extension
Explore how Gaussian Naive Bayes adapts to continuous inputs, including parameter estimation, decision boundaries, and its relation to logistic regression.
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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.