ML-and-DL-Algorithms

Math Behind ML and DL Algorithms:

Linear Regression,Polynomial Regression, Ridge Regression, Lasso Regression, Logistic Regression, Naïve Bayes, Decision Trees , Random Forest, AdaBoost, SVM, KNN, K-Means Clustering, PCA, Apriori, Q-Learning, Deep Learning(Deep Neural Network,Convolutional Neural Networks,Transformer).

Tools and Techniques used:

Loss Function and R-squared, Ordinary Least Squares, Gradient Descent, Sigmoid Function, Confusion Matrix,Accuracy and F1 Score, Information Gain, Entropy, Gini index, Bagging & Boosting, Kernel Function, Euclidean Distance, Elbow method, Covariance Matrix,Principal Components, Bayes’ Theorem, Bellman Equation, Activation Function.

Detail: