Applied Machine Learning: Problem Framing and Data Preparation

Machine learning is transforming industries from finance to healthcare, yet many projects fail before they even start due to poorly defined problems and messy data. To successfully apply machine learning, you must first master the art of framing the problem and preparing your datasets for analysis. This text-based course guides you through the essential foundations of applied machine learning. You will progress from understanding core terminology to structuring real-world problems, surveying data resources, and executing modern data preparation pipelines. What you'll learn: - Understand the fundamental concepts, terminology, and lifecycle of an applied machine learning project. - Define and frame business challenges as structured machine learning problems. - Survey and assess available data resources to ensure quality, relevance, and alignment with your goals. - Prepare and clean raw datasets using modern dataframe manipulation techniques and best practices. - Validate data pipelines to prevent common pitfalls like data leakage and overfitting. - Apply structured evaluation metrics to measure model performance against real-world objectives. The course starts with essential terminology and conceptual frameworks before moving into hands-on data preparation strategies. You will read clear explanations, analyze practical code snippets, and work through written exercises designed to build your confidence. This course is designed for beginners, professionals, and aspiring data practitioners looking to apply machine learning to their domain, with no advanced mathematical prerequisites required. Start building your foundational machine learning skills today.