Machine Learning Algorithms and Techniques

Model evaluation techniques including performance metrics for classification and regression, cross-validation methods, bias-variance tradeoff, overfitting detection, and statistical significance testing.

Comprehensive coverage of regression algorithms including simple and multiple linear regression, polynomial regression, regularization techniques (Ridge, Lasso, Elastic Net), and evaluation metrics for regression models.

Classification algorithms including logistic regression, decision trees, random forests, support vector machines, naive Bayes, k-nearest neighbors, and ensemble methods for binary and multi-class classification problems.

Unsupervised learning techniques including k-means clustering, hierarchical clustering, DBSCAN, principal component analysis (PCA), t-SNE, and association rule mining for pattern discovery in unlabeled data.

Feature engineering processes including feature creation from raw data, feature transformation and scaling, categorical encoding, feature selection techniques, and handling missing data for optimal model performance.

Advanced machine learning techniques including ensemble methods (Random Forest, AdaBoost, Gradient Boosting, XGBoost), model stacking, and advanced optimization techniques for enhanced predictive performance.

Time series analysis techniques including trend analysis, seasonality detection, ARIMA models, exponential smoothing, and machine learning approaches for time series forecasting and temporal pattern recognition.

Model optimization techniques including hyperparameter tuning methods (grid search, random search, Bayesian optimization), automated machine learning (AutoML), and model selection strategies for optimal performance.

Introduction to reinforcement learning including Markov decision processes, value functions, Q-learning, policy gradient methods, and applications in game playing, robotics, and autonomous systems.