Bias & Variance In Machine Learning | Bias Variance Tradeoff |Machine Learning Tutorial |Simplilearn
About this course
This course delves into the critical concepts of bias, variance, and their trade-offs in machine learning models, enabling learners to understand the importance of error calculation and its implications on model performance. Through engaging examples and graphical demonstrations, participants will explore the definitions and effects of bias and variance, differentiate between reducible and irreducible errors, and learn strategies to optimize model accuracy.
What you should already know
Familiarity with basic machine learning principles and Python programming language is recommended.
What you will learn
By the end of this course, learners will be able to effectively identify and analyze the bias-variance trade-off in machine learning models, optimize model performance, and implement appropriate error calculations.