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Machine Learning Bias and Model Optimization

Learn about the impact of model bias with under/overfit models, regularization techniques, and how to optimize your models and minimize error.

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About this course

This course covers model bias and variance with underfit and overfit models, and how those issues impact machine learning projects. Using some standard regularization techniques and then some common methods for model optimization and error minimization, learn how these types of ML risks can be mitigated.

What's covered
What is Deep Learning?
  • Neural network racetrack
  • Model bias and variance: under and overfit models
  • Regularization techniques
Optimization and error minimization
  • Optimization and error minimization methods
  • Backpropagation and gradient descent
  • Learning rates
  • Continual growth of AI (Deep Learning Transformers)

Curriculum17 min

About this course

This course covers model bias and variance with underfit and overfit models, and how those issues impact machine learning projects. Using some standard regularization techniques and then some common methods for model optimization and error minimization, learn how these types of ML risks can be mitigated.

What's covered
What is Deep Learning?
  • Neural network racetrack
  • Model bias and variance: under and overfit models
  • Regularization techniques
Optimization and error minimization
  • Optimization and error minimization methods
  • Backpropagation and gradient descent
  • Learning rates
  • Continual growth of AI (Deep Learning Transformers)

Curriculum17 min

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