Debugging Machine Learning Models with Python

Descripción

Debugging machine learning systems is a critical skill for building reliable, trustworthy, and high-performing AI solutions. This course teaches you how to identify, diagnose, and resolve issues throughout the machine learning lifecycle, helping you create models that are accurate, efficient, explainable, and production-ready. You will learn practical techniques to evaluate model behavior, improve performance, detect bias, manage risks, and implement testing strategies for machine learning applications. Through hands-on exploration of Python-based workflows, you will develop the ability to build reproducible pipelines, address data and concept drift, and strengthen model reliability in real-world environments. Unlike courses that focus only on model development, this course emphasizes systematic debugging and responsible AI practices. It combines foundational machine learning concepts with advanced topics such as deep learning, explainability, causality, security, privacy, and human-in-the-loop machine learning to bridge the gap between theory and industrial deployment. This course is ideal for data scientists, machine learning engineers, analysts, AI practitioners, and Python developers seeking to improve model quality and operational excellence. Learners should have basic Python programming knowledge and familiarity with machine learning concepts; the course is designed at an intermediate level.