Cross-Validation In Machine Learning | ML Fundamentals | Machine Learning Tutorial | Edureka
Taha GÖÇER
About this course
This course provides a comprehensive overview of cross-validation techniques in machine learning, covering key concepts, various methods such as k-fold, stratified k-fold, and leave-p-out cross-validation, as well as the importance of assessing model performance and addressing bias and variance within models.
What you should already know
Basic knowledge of machine learning concepts and familiarity with Python programming are required before taking this course.
What you will learn
By the end of this course, learners can expect to effectively implement cross-validation techniques, understand their significance in model evaluation, and utilize libraries like scikit-learn for practical applications.