Class notes MACHINE_LEARNING (CS-ML)

These notes provide a clear and structured introduction to Machine Learning for computer science students. This document covers core Machine Learning concepts, including real-life example,it's process, types such as supervised, unsupervised, and reinforcement learning, along with important algorithms like Linear Regression, Logistic Regression, Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes, and K-Means clustering. It also explain common technique like, normalisation, aggregation, encoding, data integration, data mapping, feature engineering. The main algorithms like, evaluation matrices in Regression models, building a model using Linear Regression,Berief details on Logistic Regression, essential exploratory algorithm, KNN algorithm, building a model using KNN, Decision Tree algorithm, buildinga model using Decision Tree, Support Vector Machine, building a model using SVM.