Applied Linear Algebra, Calculus, and Statistics for AI, ML, and Data Science Training

Applied Linear Algebra for Artificial Intelligence, Machine Learning, and Data Science

• Systems of Linear Equations
  • Singular vs non-singular matrices
  • Linear dependence, independence, and the determinant
  • Matrix row-reduction (Gaussian elimination)
  • Rank of a matrix and row echelon form
  • Systems of Linear Equations in AI, Machine Learning, and Data Science
•  Vector Operations and Linear Transformations
  • Vectors and their properties
  • Vector operations
  • Linear transformations
  • Matrix multiplication
  • Determinants and Eigenvectors
  • Machine Learning and matrices

Applied Calculus for Artificial Intelligence, Machine Learning, and Data Science

• Derivatives and optimization for AI, Machine Learning, and Data Science
  • Common derivatives and derivative properties
  • Optimization of squared loss and log loss
• CGradient Descent
  • Partial derivatives, gradients, and optimization
  • Optimization using gradient descent
• Derivatives, optimization, and gradient descent in AI, Machine Learning, and Data Science

Applied Probability & Statistics for Artificial Intelligence, Machine Learning, and Data Science

• Probability
  • Probability, Conditional Probability, Bayes Theorem, and Independence
  • Bayes Theorem, Naive Assumption, and The Naive Bayes Model
• Probability Distributions
  • Discrete and continuous distributions
  • Normal, Binomial, Bernoulli, Uniform, and Chi-Squared distributions
  • Probability Density Function and Probability Mass Function
  • Cumulative Probability, Cumulative Distribution, and Cumulative Distribution Function
  • Multivariate Probability Distributions and Covariance
  • Probability Distributions in AI, Machine Learning, and Data Science
• Statistical Sampling, Estimation, and Inference
  • Population and sample
  • Point Estimation
  • Maximum Likelihood Estimation
  • Linear regression
  • Regularization
  • Maximum a Posteriori Estimation
  • Central Limit Theorem
  • Statistical Inference: Confidence Intervals and Hypothesis Testing
  • A/B Testing
  • Statistical Sampling, Estimation, and Inference in AI, Machine Learning, and Data Science