Machine Learning 101 : Introduction to Machine Learning

What you'll learn

• The Learning Problem
• Learning from Data
• Is Learning Feasible?
• The Linear Model
• Error and Noise
• Training versus Testing
• Theory of Generalization
• The VC Dimension
• Bias-Variance Tradeoff
• Neural Networks
• Overfitting
• Regularization
• Validation
• Support Vector Machines

Munasahu

Author & Editor

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