Addition and scalar Multiplication

Mean, Standard Deviation, Median

Matrix vector multiplication

Matrix Multiplication and Properties

Inverse and Transpose

Coordinate systems

Mathematical Representation of a line (2D), plane(3D) and hyperplane (n*D)

Hyper planes and Hyper spaces

Geometric Representation of a circle (2D), sphere (3D) and hypersphere (n*D)

Equation of an ellipse (2D), ellipsoid (3D) and hyper ellipsoid (n*D)

Vector Spaces

Determinants

Eigen Vectors

Correlation, Coefficient intuition

Length and Dot Products

Linear Equations

Frequentist Interpretation

Bayesian Interpretation & Bayes Rules and Bayes Theorem

Analyze Dataset for Distributions

Refining the Qualitative and Quantitative Data.

Variation in Datasets (Univariate, Bivariate and Multivariate Data)

Population & Sample.

Gaussian/Normal Distribution and its PDF (Probability Density Function).

CDF (Cumulative Density Function) of Gaussian/Normal Distribution

Symmetric distribution, Skewness, and Kurtosis

Standard normal variate (z) and standardization.

Kernel density estimation.

Hypothesis Testing

Law of large Number?

Joint and Disjoint Outcomes

Probability Distribution

Sample Space and Complements

Probability Casting

Permutation and combination

Markov Decision Process

Discrete Sample Space (Finite and Infinite)

Events, Independence

Joint Probability and Conditional Probability

General Multiplication Rule

Inverting Probabilities

Z Score

Laws of Total Probability

Correlation and causation

Chebyshev’s inequality

Discrete and Continuous Uniform distributions.

Bernoulli and Binomial distribution