Mathematics for Machine Learning and Data Science

Notas sobre os cursos do Programa de cursos integrados Mathematics for Machine Learning and Data Science

Notebooks e Tópicos abordados:

• Linear Algebra for Machine Learning and Data Science

  • Week 1 System of linear equations: System of sentences, System of equations, System of equations as lines, A geometric notion of singularity, Singular vs nonsingular matrices, Linear dependence and independence, The determinan, System of equations (3x3), Singular vs non-singular matrices (3x3), System of equations as planes (3x3), Linear dependence and independence (3x3), The determinant (3x3)
  • Week 2 Solving system of linear equations: Solving non-singular system of equations of linear equations, solving singular systems of linear equations, graphical representation of linear system with 3 variables, solving system of equations with more variables, matrix row-reduction, row operations that preserve singularity, mathod of elimination, rank of a matrix in general, row echelon form in general, reduced row echelon form.
  • Week 3 Vectors and Linear Transformations: Vector Algebra, Vectors and their properties, Sum and difference of vectors, Distance between vectors, Multiplying a vector by a scalar, The dot product, Geometric dot product, Multiplying a matrix by a vector, Linear Transformations, Matrices as linear transformations, Linear transformations as matrices, Matrix multiplication, The identity matrix , Matrix inverse , Neural networks and matrices
  • Week 4 Determinants and Eigenvectors: Singularity and rank of linear transformations, Determinant as an area, Determinant of a product, Determinants of inverses, Bases in linear algebra, Span in linear algebra, Eigenbases, Eigenvalues and Eigenvectors

• Calculus for Machine Learning and Data Science

• Probability & Statistics for Machine Learning & Data Science

Referência

Mathematics for Machine Learning and Data Science Specialization @ DeeplearningAi

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