Machine Learning - Linear Algebra Essentials

Linear algebra is the branch of mathematics concerning linear equations, linear functions and their representations through matrices and vector spaces.
It is a much needed tool that can help you to structurize complex operations used in machine learning through notation and formalisms of linear algebra. The course will help you to understand critical essential concepts of linear algebra required in Machine Learning.
This course is designed for anyone who would like to work in this domain - machine learning and data science.

 Prerequisites:  Understanding of matrix, linear equations, basic calculus, basic probability and statistics. Python/Octave/R.
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Key Features

  • 20 hours -Customized hours instructor-led training
  • Addressing Practical needs of Machine Learning
  • Addressing Linear Algebra Libraries and Key Concepts
  • Understanding Linear Algebra Essentials
  • Study Materials & Reference Books List
  • Uses Cases included
Call us
+91-9082167982
info@eduexpress.in

Training Center
Vashi, Navi Mumbai

Course Modules

2 Hrs | Module 01: Scalars, Vector & Matrices
  • Matrix: Identity, Diagonal
  • Transpose
  • Symmetric Matrix
  • Scalar
  • Vector
  Concept & Programming exercises
2 Hrs | Module 02: Matrix Multiplication
  • Vector Vector Product
  • Matrix Vector product
  • Matrix Matrix product
  Concept & Programming exercises
2 Hrs | Module 03: Vector Space
  • Vector space
  • Linear Independency
  • Rank
  • Norm
  • Range & Null space
  • Matrix inversion
  Concept & Programming exercises
2 Hrs | Module 04: Orthogonality & Least Square
  • Inner product
  • Orthogonality
  • Orthogonal projections
  • Least square
  • Quadratic forms
  Concept & Programming exercises
2 Hrs | Module 05: Eigen Values and Eigen Vector
  • Eigen Values
  • Eigen Vector
  Concept & Programming exercises
2 Hrs | Module 06: Matrix Calculus
  • The gradient
  • The hessian
  Concept & Programming exercises
4 Hrs | Module 07: Application in Machine learning
  • Case studies
  • Machine Learning problems
  Concept & Programming exercises
4 Hrs | Module 08: Exercise
  • Applications & use cases
  Concept & Programming exercises

Why Linear Algebra?

for Machine Learning
  • Basic foundation of ML
    Without this one cannot have
    a good understanding of ML applications.
  • Intuitive Knowhow
    Vectors & Matrices can give a you
    intuitive understanding on how they work.
  • Developing a robust algorithm
    Knowing the application constraints and fine tuning the parameter w.r.t is most important element of building the algorithm.
  • Modification and understanding an existing algorithm
    One should able to use the libraries and tools to effectively understand algorithms, as these libraries uses vector and matrix operations.