Introduction to ml

This document provides an introduction to machine learning concepts including supervised and unsupervised learning, regression, classification, features, weights and bias, and linear regression. It defines machine learning as computers learning without being explicitly programmed and discusses common machine learning applications. Key machine learning types are outlined including supervised learning using labeled data for predictions, unsupervised learning with unlabeled data, deep learning using neural networks, and reinforcement learning using rewards. Regression is described as determining relationships among variables to predict quantities, using housing price prediction as an example. Linear regression for fitting a linear model to data is covered in more detail, discussing loss functions, gradient descent, and using Python code examples.