Different types of learning
Data -> Model -> Predictions
Supervised Learning
-trying to understand structure of data
-clustering
Linear Regression with gradient descent
mΣi=1*(Ypredicted – Yactual)^2
Gradient Descent – Cost Function: J(Θ)
Minimize J(Θ) … how?
import numpy import pandas def compute_cost(features, values, theta): m = len(values) sum_of_square_errors = numpy.square(numpy.dot(features, theta) - values).sum() cost = sum_of_square_errors / (2*m) return cost def gradient_descent(features, values, theta, alpha, num_iterations): cost_history = [] return theta, pandas.Series(cost_history)