ML Experiment Tracking and Model Evaluation for Beginners

Building machine learning models is only half the battle; knowing which version performed best and why is where real success lies. Without a systematic way to track your parameters, metrics, and code, machine learning development quickly becomes chaotic and unreproducible. This written course guides you through the foundational principles of ML experiment tracking and model evaluation. You will transition from manual spreadsheet logging to structured, modern MLOps workflows, enabling you to compare model runs with confidence and make data-driven decisions. In this course, you will: Learn to understand the core concepts of experiment tracking, reproducibility, and the machine learning lifecycle. Learn to log parameters, hyperparameters, and performance metrics systematically during training runs. Learn to evaluate models using key performance metrics and validation strategies. Learn to compare different training runs to identify the best-performing model configurations. Learn to apply basic data versioning concepts to ensure your experiments are fully reproducible. Learn to explore the fundamentals of modern tracking tools and MLOps platforms. Starting with essential terminology and the theory of model evaluation, this text-based guide takes you through the step-by-step process of setting up systematic tracking, analyzing training runs, and selecting the optimal model. This course is designed for aspiring data scientists, machine learning beginners, and software engineers looking to introduce structure to their AI workflows. No advanced machine learning experience is required. Start reading today to build reproducible, reliable, and well-documented machine learning experiments.