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