Complete Math, Statistics & Probability for Machine Learning

Course content

  • Set Theory
  • Combinatorics
  • Probability – Basics/General
  • Theoretical Probability
  • Empirical Probability
  • Probability – Addition Rule
  • 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 – Ordinal…
  • 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