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Mathematics for Machine Learning - Coggle Diagram
Mathematics for Machine Learning
Linear Algebra
Scalars
Vectors
Vector Operations
Vector Addition(Dot Product)
Scalar Multiplication
Projection
Matrix
Matrix Operations
Matrix Addition
Matrix Subtraction
Matrix Multiplication
Transpose
Determinants
Inverse
Scaling
Shear
Rotation
Rref method
Inverse method
Eigenvectors
Eigenvalues
Applications
Principal Component Analysis (PCA)
Pixel Data
Data prep
Singular Value Decomposition (SVD)
Natural Language Processing
Optimize Deep Learning Models
Multivariate Calculus
Differentiation
Partial Differentation
Applications
Jacobian
Hessian
Deep learning Models
Gradient Descent
Probability
Terminologies
Random Experiment
Sample Space
Events
Join Event
Disjoint Event
Distributions
Probability Density
Normal Distribution
Central Limit Theorem
Types
Marginal Probability
Joint Probability
Conditional Probability
Applications
Optimize Model
Classifications Models
Loss can also be calculated using probability
Models Built in Probability
Statistics
Terminologies
Population
Sample
Types
Descriptive
Measures of central tendency (Mean, Median, Mode)
Measures of variability (Range, Inter Quartile Range, Variance, Standard Deviation)
Entropy
Information Gain
Confusion Matrix
Inferential
Point Estimination
Interval Estimination
Hypothesis Testing
State the Hypothesis
Formulate Analysis Plan
Analyze Sample Data
Interpret Results
Null Hypothesis
Alternate Hypothesis
P-Value (Probability)
T-Value (Testing)