Overfitting in Machine Learning | Python Tutorial | Machine Learning Tutorial | Edureka
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About this course
This course provides an in-depth understanding of overfitting in machine learning, including its definition, examples, and techniques to detect and prevent it. Key topics cover the bias-variance tradeoff, signal vs. noise in datasets, and methods such as cross-validation, regularization, and early stopping to enhance model performance.
What you should already know
A foundational knowledge of machine learning concepts and Python programming is required before taking this course.
What you will learn
By the end of the course, learners will be able to identify, detect, and effectively prevent overfitting in their machine learning models, enhancing their predictive accuracy.