Introduction to Machine Learning Laboratory
Subject Overview
The practical companion to Introduction to Machine Learning, implementing regression, classification, clustering, dimensionality reduction and ensemble learning in Python — from linear/logistic regression through random forests, SVMs, perceptrons and AdaBoost. A 1-credit elective lab paper.
Unit-wise Syllabus
1 units — click WhatsApp below to get the full notes for each
Unit 1: ML model implementation in Python
Linear regression with regression line visualization, logistic regression with decision boundary, decision tree (ID3/CART) classifier, Naive Bayes classifier, random forest classifier, SVM for linearly separable classes, K-Means clustering with visualization, hierarchical clustering with dendrogram, DBSCAN clustering, PCA with classifier performance comparison, single-layer perceptron for AND/OR/XOR, AdaBoost boosting demonstration