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Machine Learning Tutorial

Supervised learning, classification, neural networks and core ML algorithms.

Neural Networks Basics

A neural network is layers of connected 'neurons' — weighted sums followed by a non-linear activation — that learn complex functions by adjusting weights via backpropagation.

  • Input layer → one or more hidden layers → output layer
  • Each connection has a learnable weight; each neuron applies an activation function (ReLU, sigmoid...)
  • Deeper networks with more layers can model more complex, non-linear relationships