Data Science with Python: Modules and Numpy

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Explores matrix operations, linear systems, solutions, and the span of vectors in linear algebra.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Introduces Eigen and Pybind libraries for linear algebra operations and C++ function binding to Python.

Covers vector spaces, bases, and dimensions, exploring key concepts for solving systems of equations.

Explains the differences between mutable and immutable objects in Python, focusing on lists, sets, and their behaviors.

Explains orthogonal projection in linear algebra, focusing on transforming non-orthogonal bases into orthogonal ones.

Covers tensor algebras, symmetric and exterior algebras, and tensor products of modules.

Covers the equilibrium conditions in 3D, system isolation, free-body diagrams, and constraints.

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Covers linear algebra basics, including matrices, change of basis, and invertible matrices.

Explores linear combinations, matrix-vector product, and matrix equation solutions.

Explains orthogonal matrices, Gram-Schmidt process, and best vector approximation in subspaces.

Covers linear systems, vector subspaces, and the kernel and image of linear applications.

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Explores multilinear groin and determinants, including the Sarrus rule and proof by induction.

Explores vector spaces, linear independence, and spanning sets in linear algebra.

Covers linear independence, bases, change of basis, and vector representation.

Covers orthogonal linear maps, orthogonal matrices, invertibility, and least squares solutions in Euclidean spaces.

Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.

Covers eigenvalues, eigenfunctions, Hermitian operators, and the measurement of observables in quantum chemistry.

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