Data Problem Solving and Machine Learning Foundations

Modern organizations rely on data-driven decision making and machine learning to solve high-impact challenges. This text-based course equips you with the foundational principles and practical skills needed to transform real-world business questions into structured machine learning solutions. You will begin by understanding core concepts, essential terminology, and problem framing techniques before advancing into data preparation, model training, and modern evaluation strategies. What you'll learn: - Understand the fundamentals of data problem framing and model development workflows - Clean, explore, and preprocess structured datasets for machine learning tasks - Train supervised and unsupervised machine learning models to solve practical problems - Apply robust evaluation metrics to validate model performance and prevent overfitting - Explore modern machine learning concepts, including baseline MLOps principles and feature engineering strategies - Translate raw analytical findings into actionable business solutions The course progresses systematically from basic data terminology to step-by-step model building exercises and real-world problem-solving scenarios. Designed specifically for beginners, no prior machine learning experience is required. Start reading today to build your practical machine learning and data problem-solving skills.