Certificate of Completion

What You Learned

• Explain the three types of machine learning — supervised, unsupervised, and reinforcement learning — and when each applies
• Compare common ML algorithms (regression, decision trees, random forests, neural networks) and identify which fits each problem type
• Design a data pipeline with proper feature engineering, train-test splitting, and cross-validation
• Evaluate model performance using accuracy, precision, recall, F1 score, and the bias-variance tradeoff
• Identify the right ML framework for each task — scikit-learn for traditional ML, PyTorch for research, TensorFlow for production
• Assess ethical risks in ML systems including algorithmic bias, fairness, and accountability