This document provides an introduction to machine learning basics for first year students. It discusses different types of data like text, audio, video, structured, and semi-structured data. It also gives examples of machine learning applications using these different data types, such as product recommendations from text data, cancer prediction from health records, and autonomous vehicles from video data. Finally, it outlines concepts for working with structured data, including exploratory data analysis, preprocessing, training and test sets, supervised learning algorithms, unsupervised learning clustering, and model evaluation.