
Explore the dot product of vectors, including perpendicular vectors with zero, a vector with itself yielding norm squared, and the scaling rule (C A)·B = C(A·B) with a practical example.
Learn how the cross product of two two-dimensional vectors equals X1 times Y2 minus X2 times Y1 and compare it with the dot product X1 times Y1 plus X2 times Y2.
Explore dot product and cross product concepts through three cases, showing how vectors A, B, and C interact via symmetry, sums, and perpendicular relationships.
Explore vector magnitudes via the norm of the cross product and dot product with the angle between A and B; learn conditions for parallelism and 3d determinant form.
Explore determinants using AD − BC and form cross product A × B from i, j, k; with A_z = B_z = 0, it becomes k(A_x B_y − A_y B_x).
Explore the identity matrix, a special matrix with zeros and ones on the main diagonal, defined in NDE dimensions; see I3 and I5 examples, and note NDE ≥ 2.
Explore inverse matrices and matrix multiplication, deriving the inverse of A as 1/(ad-bc) [d, -b; -c, a], and verify A times its inverse equals the identity matrix, with N-dimensional generalization.
Learn to compute a matrix inverse using the adjugate method, where the adjugate is the transpose of the cofactor matrix defined by (-1)^{i+j} M_ij, revealing a chessboard sign pattern.
Compute minors by removing row i and column j, form cofactors with (-1)^{i+j}, transpose to get adjugate, and use A^{-1} = (1/det A) adj(A) (simplifies in 2d).
Learn Gauss-Jordan method to find a matrix inverse by augmenting with the identity and applying row operations, illustrated on a 3×3 example and inverse verification.
Explore how to compute the transpose and inverse of a matrix, and verify that the inverse of the transpose equals the transpose of the inverse through a concrete example.
Compute the inverse of a 2x2 matrix A and show that scaling A by a nonzero K yields (K A)^{-1} = (1/K) A^{-1}.
Show that for a symmetric matrix, its inverse is also symmetric by applying (A^{-1})^T = (A^T)^{-1} and the fact that A^T = A.
Describe the generalized inverse A+ for non-invertible matrices via singular value decomposition, with A = U Σ V^T and A+ = V Σ+ U^T.
Unlock the power of Linear Algebra for adult learners ready to deepen their intuition or master the foundations of modern technology.
Welcome to my course! Unlock the potential of matrices with a step-by-step journey through vectors, determinants, inverses, and pseudoinverses, explained clearly and intuitively. This comprehensive course is designed for adult learners who want to strengthen their mathematical foundations, prepare for advanced STEM education, or gain the technical confidence needed for data-driven careers.
Whether you are revisiting these concepts for career growth in Engineering, preparing for university-level Data Science, or simply want to understand the mechanics of the digital world, this course will guide you through the essentials of Linear Algebra in a structured, supportive way. You will not only master the calculations but also see how matrix transformations connect to real-world problem solving and the core of modern Artificial Intelligence systems.
What You’ll Learn:
Master Vector Operations: Understand addition, scalar multiplication, and the geometric intuition of vectors.
Analyze Determinants: Learn to calculate and interpret determinants to understand how matrices scale space.
Navigate Matrix Properties: Master the roles of Identity and Transpose matrices in mathematical transformations.
Solve with Inverses: Develop a deep understanding of Matrix Inversion and the specific conditions for invertibility.
Explore the Pseudoinverse: Learn to handle non-square matrices and "unsolvable" systems - a critical skill for AI and Data Science.
See the AI Connection: Understand how these algebraic principles underpin machine learning and data processing.
Who This Course Is For ?
Adult learners preparing for higher education or refreshing core technical math skills.
Aspiring Data Scientists and AI enthusiasts who want to understand the math behind the algorithms.
Engineering students looking for a clear, intuition-first review of matrix operations.
Professionals in STEM who want to bridge the gap between abstract math and professional application.
Anyone who wants a fresh start with Linear Algebra through a clear and structured teaching style.
What’s Included:
Clear, easy-to-follow video lessons focused on intuition and clarity.
Quizzes and exercises to reinforce your mastery of matrix operations.
Downloadable PDFs for quick reference, notes, and extra practice.
Real-life insights, including how these concepts apply to modern AI and Engineering.
Lifetime access and certificate of completion to showcase your new skills.
Start learning today and see how mastering Matrices and Linear Algebra can transform your confidence, open doors in tech, and help you understand the math that powers the future of AI.