Machine Learning 101 : Introduction to Machine Learning

Description

Machine Learning 101 : Introduction to Machine Learning

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Description

Filesize : 17.07 GB

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
Kernel Methods
Radial Basis Functions
Three Learning Principles
Epilogue
What is learning?
Can a machine learn?
Identify basic theoretical principles, algorithms, and applications of Machine Learning
Elaborate on the connections between theory and practice in Machine Learning
Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations