?What is the importance of mathematics in everyday life

Question

Accepted Answer

Mathematics is essential for solving everyday problems, managing finances, making informed decisions, and understanding the world. It plays a crucial role in fields like science, engineering, economics, and technology.

Excellent		23 Reviews
Good		21 Reviews
medium		2 Reviews
Acceptable		0 Reviews
Not Good		0 Reviews

Mathematics basics of Machine learning

Mathematics

How to Get The Certificate

Tutorial 01 Course Outline of Linear Algebra | Mathematics of Machine Learning

Tutorial 02 Vector vs Scalar vs Point

Tutorial 03 Vector Operation in Linear Algebra | Vector Addition Subtraction Scalar Multiplication

Tutorial 04 Vector Norm in Linear Algebra | L1 Norm L2 Norm Infinity Norm of Vector

Tutorial 05 Unit Vector and Position Vector in Linear Algebra

Tutorial 06 Dot Product of two Vectors | Vector multiplication in Linear Algebra

Tutorial 07 Cross Product of two vectors | Vector multiplication in Linear Algebra

Tutorial 08 Vector Projection in Linear Algebra | Vector Projection Application in Machine Learning

Tutorial 09 Types of Vector in Linear Algebra | Vectors in Machine Learning

Tutorial 10 Vector Space in Linear Algebra | Vector Space in machine learning

Tutorial 11 Linear Combination of the vector | Spanning Set of a vector space

Tutorial 12 Linearly Dependent and Linearly Independent Vectors in Linear Algebra

Tutorial 13 Line VS Plane Vs Hyperplane in Linear Algebra | Point Segment Colinear Copular

Tutorial 14 Practical Linear Algebra for Machine Learning Python

Tutorial 15 Matrix and its Types in Linear Algebra | Important types of Matrix in Machine Learning

Tutorial 16 Matrix Addition Subtraction and Scalar Multiplication

Tutorial 17 Matrix Multiplication | Inner Product Cross Product of Matrix | Hadamard product matrix

Tutorial 18 Matrix Operations in Linear Algebra | Matrix Power Rank Determinant Inverse

Tutorial 19 Linear Equation and System of Linear Equation in Linear Algebra for Machine Learning

Tutorial 20 Eigenvalue and Eigenvector in Linear Algebra for Machine Learning

Tutorial 21 Linear Transformation in Linear Algebra | Linear Map for Machine Learning

Tutorial 22 Image and Kernel of a Linear Transformation for Machine Learning

Tutorial 23 The Gram Schmidt Process | Gram Schmidt Orthogonalization Process in Linear Algebra

Tutorial 24 Eigendecomposition of a matrix in Linear Algebra for Machine Learning

Tutorial 25 Matrix Norm in Linear Algebra | 1 Norm Infinity Norm Frobenius Norm Euclidean Norm

Tutorial 26 Matrix Operations in Linear Algebra | Matrix Operations using Numpy Python with Example

Tutorial 27 Rank Inverse Trace Flatten Eigenvalues and Eigenvectors of Matrix using Python

Tutorial 28 LU Decomposition Method in Linear Algebra for Machine Learning

Tutorial 29 Singular Value Decomposition in Linear Algebra for Machine Learning

Tutorial 30 Pseudo Inverse vs Singular Value Decomposition | Pseudo Inverse of a Matrix

Tutorial 31 Practical LU Decomposition Singular Value Decomposition and Pseudo Inverse using Python

Tutorial 32 Fisher Discriminant Analysis FDA Maths | FDA vs LDA

Tutorial 33 Part 1 Linear Discriminant Analysis Mathematics and Working | LDA in Machine Learning

Tutorial 34 Part II Linear Discriminant Analysis using IRIS dataset in PYTHON | LDA using Python

Tutorial 35 Part I Maths behind PCA | Principal Component Analysis in easy way

Tutorial 36 Part II Principal Component Analysis using IRIS dataset in Python | PCA using Python

Tutorial 37 Part I Nine Distance Measure in Machine Learning | Euclidean cosine Hamming distance

Tutorial 38 Part II Nine Distance Measure in Machine Learning | Minkowski Manhattan Chebyshev in ML

Tutorial 39 Part III Nine Distance Measure in Machine Learning | Jaccard Haversine Sørensen Dice

Tutorial 40 Part IV Nine Distance Measure in Machine Learning using Python

Real numbers

Statistics

Quadratic Equation

Polynomials

Pair of Linear Equations in Two Variables

Coordinate Geometry

Arithmetic Progression

Areas Related to Circles

Circles

Application of Trigonometry

Triangles

Surface Area and Volumes

Youth Empowerment

Algebra Full data

Algebra

Discrete Mathematics