Machine Learning Algorithms: KNN, Naive Bayes, Decision Trees, and SVM

Modern machine learning relies on understanding how core predictive algorithms process data, make decisions, and generalize to new inputs. This text-based course guides you through the intuition, mathematical concepts, and practical code implementations behind four fundamental supervised learning models. You will gain a clear conceptual understanding of how distance metrics, probability theory, tree structures, and hyperplanes power modern classification tasks, allowing you to select and tune the right model for any dataset. What you'll learn: - Understand the core principles and mathematical foundations behind KNN, Naive Bayes, Decision Trees, and Support Vector Machines - Implement classification models from scratch and using modern Python libraries like scikit-learn - Preprocess data properly with scaling, categorical encoding, and feature selection - Evaluate model performance using confusion matrices, precision-recall metrics, and cross-validation techniques - Tune essential hyperparameters to prevent overfitting and improve generalization on unseen data - Compare algorithm trade-offs regarding training speed, memory usage, and interpretability The course begins with foundational machine learning terminology and data preparation principles before walking through each algorithm step by step with clear text explanations and code examples. This course is designed for beginners in data science and software development who want a solid conceptual and practical foundation in machine learning algorithms without overwhelming complexity. Start reading today to build a strong foundation in essential machine learning models.