Machine Learning Foundations

What you'll learn

• Core ML Paradigms: Understand the differences and appropriate use cases for supervised, unsupervised, and reinforcement learning.
• Data Preparation & Feature Engineering: Handle missing data, encode categorical features, scale numerical data, and build robust preprocessing pipelines.
• Supervised Learning: Implement, train, and evaluate classification and regression models (Linear Regression, Logistic Regression, Decision Trees, Random Forests).
• Unsupervised Learning: Discover hidden structures and patterns using clustering (K-Means) and dimensionality reduction techniques (PCA).
• Model Evaluation & Optimization: Apply cross-validation, prevent overfitting (regularization), and assess model performance using metrics like accuracy, precision, recall, F1-score, and Mean Squared Error (MSE).