P.01Implement K-Nearest Neighbors classification using scikit-learn with data visualization, model training, and performance evaluation on real datasets.
P.02Understand the KNN algorithm — how it works, distance metrics, choosing K, and its applications in both classification and regression tasks.
P.03Compare Naive Bayes, SVM, Decision Tree, and Random Forest for email spam detection with a complete Python pipeline from data loading to evaluation.
P.04Learn Occam's Razor, regularization, pruning, ensemble methods, cross-validation, Bayesian model selection, genetic algorithms, and more to boost ML performance.
P.05Master MAE, MSE, R², RMSE, accuracy, precision, recall, F1-score, AUC-ROC, and confusion matrices with formulas and Python code examples.
P.06Understand the bias-variance tradeoff in machine learning with mathematical formulas, visual explanations, and strategies to find the right balance.
P.07Build an insurance cost prediction model using multivariate linear regression with one-hot encoding, evaluation metrics, and residual analysis.
P.08EditorPickMaster 7 encoding techniques for categorical variables — one-hot, label, dummy, binning, count, frequency, and target encoding with Python examples.
P.09Build a linear regression model from scratch using scikit-learn, with data visualization, feature selection, and model evaluation metrics.
P.10Explore reinforcement learning fundamentals — agents, environments, states, actions, Q-learning, SARSA, Actor-Critic, and deep RL approaches.
P.11Understand unsupervised learning methods including clustering, dimensionality reduction, anomaly detection, and generative models with practical examples.
P.12Explore derivatives, integrals, multivariate calculus, optimization, and differential equations with Python implementations using SymPy and NumPy.