Building, Evaluating, and Operationalizing ML Models

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will dive into the entire process of building, evaluating, and operationalizing machine learning (ML) models. Starting with data exploration, you'll learn how to select the right algorithms for regression and classification tasks and fine-tune them for optimal performance. As you progress, you'll gain hands-on experience with tools like Azure ML Studio, experimenting with model customization, feature engineering, and advanced algorithms such as XGBoost and Neural Networks. You'll also discover how to evaluate models, optimize performance, and deploy them effectively. The course offers practical demos, empowering you to implement everything from simple models to complex pipelines in ML workflows. Throughout the course, you'll explore model evaluation techniques, including cross-validation and performance metrics, and learn how to address issues like overfitting and model drift. You'll also engage with ML-Ops concepts, discovering how to structure scalable pipelines, automate workflows, and manage the lifecycle of your models. This course is perfect for those looking to gain real-world ML skills, especially those interested in Azure ML Studio and automated pipelines. Whether you’re starting with basic ML concepts or expanding your knowledge, this course provides a comprehensive guide. You’ll learn to make data-driven decisions while optimizing the end-to-end machine learning lifecycle. By the end, you'll be prepared to build and deploy models that are both efficient and scalable, while also staying on top of versioning and performance monitoring.