Maths 2 Book: Linear Algebra
Welcome to the digital textbook for Mathematics 2. This course provides a deep dive into the theoretical and practical aspects of Linear Algebra, essential for understanding modern Data Science algorithms.
Course Content
Chapters
All chapters of the Linear Algebra textbook.
Vector and Matrices
Introduction to vectors, matrices, and their fundamental operations in linear algebra.
Solving Systems of Linear Equations
Mastering techniques to solve linear systems: Cramer's Rule, Inverse Matrix, and Gauss Elimination.
Introduction to Vector Space
Formal definition of vector spaces, axioms, and subspaces.
Basis and Dimension
Understanding the building blocks of vector spaces: Linear Independence, Spanning Sets, Basis, and Dimension.
Rank and Nullity
Exploring the fundamental subspaces of a matrix and the Rank-Nullity Theorem.
Linear Transformation
Mapping vector spaces: Homomorphisms, Isomorphisms, and Matrix Representations.
Equivalence and Similarity
Comparing matrices: When are two matrices really the same thing in disguise?
Affine Subspaces
Moving beyond the origin: Affine subspaces and mappings.
Inner Product Space
Geometry in vector spaces: Angles, Lengths, and Orthogonality.
All Chapters in this Book
Mathematics 1
Foundation level mathematics covering Sets, Relations, Functions, and Calculus.
Mathematics 2
Advanced mathematics for Data Science covering Linear Algebra, Vector Spaces, and Matrices.
Maths 2 Book: Linear Algebra
A complete textbook course on Linear Algebra for Data Science, covering Vectors, Matrices, and Vector Spaces.
Statistics 2
Advanced statistical methods including Hypothesis Testing, Regression, and ANOVA.
Stats 2 Book: Probability & Statistics
A comprehensive textbook covering probability, distributions, inference, and regression.
Stats 1 Vol 1: Descriptive Statistics
A comprehensive guide to the foundational concepts of statistics, data organisation, and probability-based counting.