Machine Learning with Small Data: Practical Strategies

You do not always need millions of data points to build effective AI systems. In many real-world scenarios, from specialized diagnostics to niche business applications, working with limited data is the norm. This text-based course teaches you how to design, train, and evaluate machine learning models successfully when data is scarce. You will transition from struggling with overfitting to confidently applying modern techniques that maximize the value of every single data point. What you will learn: Understand the core challenges of small datasets, including overfitting, high variance, and evaluation bias; Apply data augmentation and synthetic data generation techniques to expand your training sets; Leverage transfer learning and pre-trained foundation models to achieve high accuracy with minimal custom data; Configure regularization, ensemble methods, and cross-validation strategies optimized for small-sample regimes; Implement active learning workflows to prioritize which data points to label next. The course begins with foundational concepts of sample complexity and overfitting before moving into practical, hands-on strategies for data expansion, model selection, and robust validation. Designed for beginners with a basic understanding of programming and core machine learning concepts who want to solve real-world problems where data is limited. Start mastering machine learning for the real world, where data is rarely perfect or plentiful.