Data Processing, Machine Learning, and Model Evaluation

Raw data is rarely ready for machine learning, and building a model is only half the battle—you must also know how to evaluate its real-world performance. This text-based course guides you through the essential pipeline of preparing data, training models, and measuring their success. You will transition from working with messy datasets to confidently structuring them for machine learning algorithms. By understanding the core principles of feature engineering, model selection, and validation metrics, you will build reliable systems that generalize well to new, unseen data. What you will learn: Understand foundational data preprocessing techniques, including handling missing values, scaling, and encoding categorical variables; Apply modern feature engineering methods to optimize your datasets for machine learning algorithms; Build and train classical machine learning models using industry-standard Python libraries; Evaluate model performance using key metrics such as precision, recall, F1-score, and ROC-AUC; Implement robust validation strategies like cross-validation to prevent overfitting; Explore modern MLOps concepts for tracking experiments and managing model versions. The course begins with essential data processing concepts and terminology before advancing to practical modeling techniques and rigorous evaluation strategies. You will read through clear explanations and structured code examples designed to solidify your understanding of the machine learning workflow. This course is designed for beginners who want a structured introduction to data science and machine learning, with no prior modeling experience required. Start reading today to build a strong foundation in data preparation and machine learning evaluation.