Linear Algebra ,Probability ,Statistics and Calculus for Machine Learning.
Linear Algebra: Vectors Operations, Cosine Similarity, Matrix Operations, EigenValues and Eigen Vectors, Singular Value Decomposition, Principal Component Analysis, Gradient Descent.
Probability and Statistics: Bayes’ Theorem, Gaussian Distribution, Information Gain and Entropy, Gini index, Measures of Central Tendency, Variance and Standard Deviation, Covariance, Correlation matrix, Chi-square Test, Interquartile range, Z- Score ,Confusion Matrix.
Calculus: Partial derivatives, Chain rule and Power rule apply on Gradient Descent algorithm that minimizes an error function.