Machine Learning Mathematics Read Discuss Courses Practice Improve Improve Improve Like Article Like Save Article Save Report issue Report Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. Math is the core concept in machine learning which is used to express the idea within the machine learning model. Mathematics for Machine Learning In this tutorial, we will look at different mathematics concepts and will learn about these modules from basic to advance with the help particular algorithm. Linear Algebra and Matrix Regression Geometry Dimensionality Reduction Vector Calculus Vector Models Probability Distribution Miscellaneous Linear Algebra and Matrix Linear Algebra is an algebra extension to an undefined number of dimensions. Linear Algebra concerns the focus on linear equation systems. Vectors and Matrices Matrix Introduction Matrix Addition Matrix Addition using NumPy Arrays Matrix Multiplication Matrix Multiplication using Python Matrix Manipulation using NumPy Arrays Inverse of a Matrix Evaluating Inverse using NumPy Arrays Transpose of a Matrix Evaluating Transpose using NumPy Arrays Properties of Matrix Determinant Trace System of Linear Equations System of Linear Equation Solving Linear Equations using Gaussian Elimination LU Decomposition of Linear Equation Matrix Inversion Matrix Factorization Gram-Schmidt Process QR Decomposition Cholesky Decomposition Singular Value Decomposition Matrix Factorization Diagonalization Eigenvalues and Eigenvectors Eigenspace Vector Spaces Vector Operations Vector Spaces and SubSpaces Basis and Dimension Row Echelon Form Linear Mappings Least Square and Curve Fitting Affine Spaces Statistics Statistics is the collection of data, tabulation, and interpretation of numerical data, and it is applied mathematics concerned with data collection analysis, interpretation, and presentation. Mean, Standard Deviation, and Variance Calculating Mean, Standard Deviation, and Variance using Numpy Arrays Sample Error and True Error Bias Vs Variance and Its Trade-Off Hypothesis Testing T-test Paired T-test p-value F-Test z-test Confidence Intervals Correlation and Covariance Correlation Coefficient Covariance Matrix Normal Probability Plot Q-Q Plot Residuals Leverage Plot Robust Correlations Hypothesis Testing Null and Alternative Hypothesis Type 1 and Type 2 Errors p-value interaction Parametric Hypothesis Testing T-test Paired Samples t-test ANOVA Test Non-Parametric Hypothesis Testing Mann-Whitney U test Wilcoxon signed-rank test Kruskal-Wallis test Friedman test Theory of Estimation Difference between Estimators and Estimation Methods of Estimation Method of Moments Bayesian Estimation Least Square Estimation Maximum Likelihood Estimation Likelihood Function and Log-Likelihood Function Properties of Estimation Unbiasedness Consistency Sufficiency Completeness Robustness Confidence Intervals Geometry Geometry is the branch of mathematics that deals with the forms, angles, measurements, and proportions of ordinary objects. Vector Norms Inner, Outer, Cross Products Distance Between Two Points Distance Measures Euclidean Distance Manhattan Distance Minkowski Distance Chebysev Distance Similarity Measures Cosine Similarity Jaccard Similarity Pearson Correlation Coefficient Kendall Rank Correlation Measure Pearson Product-Moment Correlations Spearman’s Rank Correlation Measure Orthogonality and Orthogonal Projections Orthogonality and Orthonormal Vectors Orthogonal Projections Rotations Geometric Algorithms Nearest Neighbor Search Voronoi diagrams Delaunay Triangulation Geometric intersection and Proximity queries Constraints and Splines Box-Cox Transformations Box-Cox Transformation using Python Fourier transformation Properties of Fourier Transform Inverse Fast Fourier Transformation Calculus Calculus is a subset of mathematics concerned with the study of continuous transition. Calculus is also known as infinitesimal calculus or “infinite calculus.” The analysis of continuous change of functions is known as classical calculus Differentiation Implicit Differentiation Inverse Trigonometric Functions Differentiation Logarithmic Differentiation Partial Differentiation Advanced Differentiation Mathematical Intuition Behind Gradients and their usage Implementation of Gradients using Python Optimization Techniques using Gradient Descent Higher-Order Derivatives Multivariate Taylor Series Application of Derivation Application of Derivative – Maxima and Minima Absolute Minima and Maxima Constrained Optimization Unconstrained Optimization Constrained Optimization – Lagrange Multipliers Newton’s Method Uni-variate Optimization Multivariate Optimization Convex Optimization Lagrange’s Interpolation Area Under Curve Probability and Distributions Probability and distributions are statistical functions that describe all the possible values. Probability Chance and Probability Addition Rule for Probability Law of total probability Bayes’ Theorem Discrete Probability Distributions Discrete Uniform Distribution Bernoulli Distribution Binomial Distribution Poisson Distribution Continuous Probability Distributions Continuous Uniform Distribution Exponential Distribution Normal Distribution Beta Distribution Beta Distribution of First Kind Beta Distribution of Second Kind Gamma Distribution Sampling Distributions Chi-Square Distribution F – Distribution t – Distribution Central Limit Theorem Implementation of Central Limit Theorem Law of Large Numbers Change of Variables/Inverse Transformation Regression Regression is a statistical process for estimating the relationships between the dependent variables or criterion variables Parameter Estimation Bayesian Linear Regression Quantile Linear Regression Normal Equation in Linear Regression Maximum Likelihood as Orthogonal Projection Dimensionality Reduction Dimensionality reduction is a technique to reduce the number of input variables in training data. Introduction to Dimensionality Reduction Projection Perspective in Machine Learning Eigenvector Computation and Low-Rank Approximations Mathematical Intuition Behind PCA PCA implementation in Python Latent Variable Perspective Mathematical Intuition Behind LDA Implementation of Linear Discriminant Analysis (LDA) Mathematical Intuition Behind GDA Implementation of Generalized Discriminant Analysis (GDA) Mathematical Intuition Behind t-SNE Algorithm Implementation of the t-SNE Algorithm Whether you're preparing for your first job interview or aiming to upskill in this ever-evolving tech landscape, GeeksforGeeks Courses are your key to success. We provide top-quality content at affordable prices, all geared towards accelerating your growth in a time-bound manner. Join the millions we've already empowered, and we're here to do the same for you. Don't miss out - check it out now! Last Updated : 24 May, 2023 Like Article Save Article Next Matrices Please Login to comment...