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Learn how K‑NN and Naive Bayes work from two opposite philosophies: geometric similarity and probabilistic reasoning. Discover their advantages, limitations, examples, and when to use each classifier in real machine learning projects.
Learn how Softmax transforms raw scores into probabilities and how multinomial logistic regression extends binary logistic regression to multi‑class problems. Understand logits, multiclass cross‑entropy, decision boundaries, and when Softmax is the right choice.
Learn why logistic regression needs regularization, how log-loss works, and how Ridge, Lasso, and Elastic Net prevent sigmoid saturation and overconfident predictions. Discover when to use each method and how regularization shapes decision boundaries
Learn how Ridge and Lasso regularization control overfitting by penalizing large coefficients. Understand the difference between L1 and L2 penalties, when to use each method, and why lambda is key to balancing flexibility and stability in regression models.
Learn how polynomial regression models nonlinear relationships by transforming the input variable into polynomial features. Understand how it trains using the same gradient descent as linear regression, why higher-degree polynomials risk overfitting, and how cross‑validation helps choose the right degree.
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