Colleagues, in the
Linear Algebra for Machine Learning and Data Science you will learn to represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence, how to apply common vector and matrix algebra operations like dot product, inverse, and determinants, and how ro express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems Skills you'll gain include Eigenvalues And Eigenvectors, Linear Equation, Determinants, Machine Learning and Linear Algebra. We also recommend a basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science. If you are already familiar with the concepts of linear algebra, Course 1 will provide a good review, or you can choose to take Course 2: Calculus for Machine Learning and Data Science and Course 3: Probability and Statistics for Machine Learning and Data Science, of this specialization. A basic familiarity with Python (loops, functions, if/else statements, lists/dictionaries, importing libraries), as labs use Python and Jupyter Notebooks to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science is recommended. Week 1: Systems of linear equations - Linear Algebra Applied, System of sentences, System of equations, System of equations as lines and planes,, A geometric notion of singularity, Singular vs nonsingular matrices, Linear dependence and independence, The determinant, Week 2: Solving systems of linear equations, Solving non-singular system of linear equations, Solving singular system of linear equations, Solving system of equations with more variables, Matrix row-reduction, Row operations that preserve singularity, The rank of a matrix,, The rank of a matrix in general, Row echelon form, Row echelon form in general, Reduced row echelon form,, The Gaussian Elimination Algorithm, Week 3: Vectors and Linear Transformations - Machine Learning Motivation, Vectors and their properties, Vector operations, The dot product, Geometric Dot Product, Multiplying a matrix by a vector, Matrices as linear transformations, Linear transformations as matrices, Matrix multiplication, The identity matrix, Matrix inverse, Which matrices have an inverse?, Neural networks and matrices, and Week 4: Determinants and c - Singularity and rank of linear transformations, Determinant as an area, Determinant of a product and inverses, Bases in Linear Algebra, Span in Linear Algebra, Eigenbases, Eigenvalues and Eigenvectors, Calculating Eigenvalues and Eigenvectors, On the Number of Eigenvectors, Dimensionality Reduction and Projection, Motivating PCA, Variance and Covariance, Covariance Matrix, PCA - Overview, PCA - Why It Works, PCA - Mathematical Formulation and Discrete Dynamical Systems. Enroll today (teams & execs welcome): https://imp.i384100.net/eKmbx6
Download your free AI-ML-DL - Career Transformation Guide.
For your listening-reading pleasure:
1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)
2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle)
3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle)
Much career success, Lawrence E. Wilson - AI Academy (share with your team)
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