Machine Learning – Unit IV: LDA, Perceptron, SVM & RegressionFree

Complete Machine Learning Unit IV notes covering Linear Discriminant Analysis (LDA), Perceptron Classifier, Neural Network fundamentals, Perceptron Learning Algorithm, Support Vector Machines (SVM), and Regression techniques.

Topics include LDA classification and dimensionality reduction, LDA assumptions, advantages and applications, Perceptron components, weights and bias, activation functions, single-layer and multi-layer perceptrons, MLP, backpropagation, SVM hyperplanes, support vectors, margins, kernels, hard and soft margins, linear and non-linear SVM, regression, linear regression, multiple linear regression, polynomial regression, decision tree regression, random forest regression, support vector regression, ridge regression and lasso regression.

The material also explains best-fit lines, cost functions, gradient descent, regularization, and backpropagation, including forward and backward passes, weight updates, vanishing gradients, exploding gradients, and overfitting.