# Complete Math, Statistics & Probability for Machine Learning

## Course content

• Set Theory
• Combinatorics
• Probability – Basics/General
• Theoretical Probability
• Empirical Probability
• Probability – Multiplication Rule
• Conditional Probability
• Theorem of Total Probability
• Probability – Decision Tree
• Bayes’ Theorem
• Sensitivity and Specificity in Probability: Diagnostic Test Performance
• Binomial, Poisson & Normal Distributions, Z-Score, Skewness, Kurtosis
• Geometric Distribution
• Hypergeometric Distribution
• Markov Chain
• Linear Algebra – Matrices
• Linear Algebra – Determinant
• Linear Algebra – EigenVaIue and EigenVector
• Euclidean Distance and Manhattan Distance
• NumPy – Recommendation! Recommendation!! Recommendation!!!
• Statistics
• Statistics – Mean
• Statistics – Weighted Mean
• Statistics – Properties of Mean
• Statistics – Mean Frequency Distribution
• Statistics – Median
• Statistics – Median Frequency Distribution
• Statistics – Mode
• Statistics – Mode Frequency Distribution
• Statistics – Measurement of Spread
• Statistics – Range
• Statistics – Mean Deviation
• Statistics – Variance & Standard Deviation
• Statistics – Variable I Dependent- Independent – Moderating – Ordinalâ€¦
• Statistics – Correlation
• Statistics – Regression & Collinearity
• Statistics – Pearson and Spearman Correlation Methods
• Statistics – Regression Error Metrics
• Indices & Logarithms
• Entropy in Machine Learning
• Information Gain
• Surprise in Machine Learning
• Loss Function, Cost Function and Error Function
• Mean Squared Error Loss Function
• Mean Absolute Error Loss Function
• Huber Loss Function
• Cross Entropy Loss Function
• Categorical Cross-Entropy Loss Function
• Hinge Loss Function
• Calculus – Introduction to Differentiation
• Calculus – Derivatives By First Principle
• Calculus – Second Derivatives
• Calculus – Special Derivatives
• Calculus – Differentiation Using Chain Rule
• Calculus – Differentiation Using Product Rule
• Calculus – Differentiation Using Chain and Product Rules I Examples
• Calculus – Differentiation Using Quotient Rule
• Calculus – Differentiation Using Quotient and Chain Rules I Examples
• Integration – Introduction
• Integration – Indefinite Integrals
• Integration – Indefinite Integrals II
• Integration – Definite Integrals I
• Integration – Definite Integrals II
• Area Under Curve – Using Integration
• ARCHIVED – Set Theory
• ARCHIVED – Combinatorics
• ARCHIVED – Probability – Basics/GeneraI
• ARCHIVED – Theoretical Probability
• ARCHIVED – Empirical (Experimental) Probability
• ARCHIVED – Probability Addition Rules
• ARCHIVED – Probability Multiplication Rule
• ARCHIVED – Probability – Tons of Exercises & Solutions
• ARCHIVED – Conditional Probability
• ARCHIVED – Theorem of Total Probability
• ARCHIVED – Bayes’ Theorem
• ARCHIVED – Bayes’ Theorem, Total Probability and Depend Events – Decision Tree
• ARCHIVED – Linear Algebra – Matrices
• BONUS SECTION
Posted in Uncategorized