Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
For continuous linear predictions
A linear model is defined as
y = w * x + b
Mean Squared Error
MSE = 1/n * SUM(xi-x)²
The goal is to minimize the Mean Squared Error to fit the Function to the dataset (Function approximation).
Steps to take:
- Take the derivative of MSE for calculating gradients
- Compute error/ gradients with training data (Supervised learning)
- Backpropagate the gradients with Learning Rate lr by using gradient descent and adjust weights and bias (w, b)
- Iterate until minimum is reached
After the training the model is used for making predictions (inference)
For classification tasks which uses a sigmoid function. Uses a similar compuational process as linear regression.
Neural networks introduces non linearities via an Activation Function for use in non linear problems (Classification and Regression)
Equation of one Neuron:
y = (w * x + b) + Activation-Function
Combine a number of neurons to create a Neural Network (MLP - Multi Layer Perceptron)