Great Learning's free course on "Regularization in Machine Learning" is a comprehensive introduction to the concept of regularization and how it can be used to prevent overfitting in machine learning models. The course is designed for learners with a basic understanding of machine learning and statistical concepts.

The course covers various regularization models, including L1, L2, and Elastic Net. It also explains how regularization can help to reduce model complexity and improve the generalization of machine learning models. Learners will gain a deep understanding of the error function in terms of contour graphs, which will help them visualize and analyze model performance.

The course also covers the concept of pipelines, which is a powerful tool for automating machine learning workflows. Learners will learn how to build and make pipelines and how to use them for data preprocessing, feature extraction, and model training.

The course also covers hyperparameters and tuning, which are essential for optimizing machine learning models. Learners will gain a solid understanding of how to use randomized search CV to explore the hyperparameter space efficiently.

The course content is well-structured and easy to follow, with practical exercises and projects to help learners apply the concepts in real-world scenarios. The course is accessible from anywhere, making it a great option for busy professionals or students.

In conclusion, Great Learning's free course on "Regularization in Machine Learning" is an excellent resource for anyone interested in learning about regularization and its application in machine learning. With its comprehensive coverage of the subject and practical exercises, learners will gain valuable skills that they can apply in their professional or academic endeavors.