Gaussian Elimination, Eigenvalues, Numerical Integration, Interpolation, Differential Equations and Operations Research.
Description
This course is about numerical methods and optimization algorithms in Python programming language.
*** We are NOT going to discuss ALL the theory related to numerical methods (for example how to solve differential equations etc.) – we are just going to consider the concrete implementations and numerical principles ***
The first section is about matrix algebra and linear systems such as matrix multiplication, gaussian elimination and applications of these approaches. We will consider the famous Google’s PageRank algorithm.
Then we will talk about numerical integration. How to use techniques like trapezoidal rule, Simpson formula and MonteCarlo method to calculate the definite integral of a given function.
The next chapter is about solving differential equations with Euler’smethod and RungeKutta approach. We will consider examples such as the pendulum problem and ballistics.
Finally, we are going to consider the machine learning related optimization techniques. Gradient descent, stochastic gradient descent algorithm, ADAGrad, RMSProp and ADAM optimizer will be discussed – theory and implementations as well.
*** IF YOU ARE NEW TO PYTHON PROGRAMMING THEN YOU CAN LEARN ABOUT THE FUNDAMENTALS AND BASICS OF PYTHON IN THA LAST CHAPTERS ***
Section 1 – Numerical Methods Basics
 numerical methods basics
 floating point representation
 rounding errors
 performance C, Java and Python
Section 2 – Linear Algebra and Gaussian Elimination
 linear algebra
 matrix multiplication
 Gausselimination
 portfolio optimization with matrix algebra
Section 3 – Eigenvectors and Eigenvalues
 eigenvectors and eigenvalues
 applications of eigenvectors in machine learning (PCA)
 Google’s PageRank algorithm explained
Section 4 – Interpolation
 Lagrange interpolation theory
 implementation and applications of interpolation
Section 5 – Root Finding Algorithms
 solving nonlinear equations
 root finding
 Newton’s method and bisection method
Section 6 – Numerical Integration
 numerical integration
 rectangle method and trapezoidal method
 Simpson’s method
 MonteCarlo integration
Section 7 – Differential Equations
 solving differentialequations
 Euler’s method
 RungeKutta method
 pendulum problem and ballistics
Section 8 – Numerical Optimization (in Machine Learning)
 gradient descent algorithm
 stochastic gradient descent
 ADAGrad and RMSProp algorithms
 ADAM optimizer explained
*** IF YOU ARE NEW TO PYTHON PROGRAMMING THEN YOU CAN LEARN ABOUT THE FUNDAMENTALS AND BASICS OF PYTHON IN THA LAST CHAPTERS ***
Thanks for joining my course, let’s get started!
Who this course is for:
 This course is meant for student with quantitative background or software engineers who are interested in numerical methods
What you’ll learn

Understand linear systems and Gaussian elimination

Understand eigenvectors and eigenvalues

Understand Google’s PageRank algorithm

Understand numerical integration

Understand MonteCarlo simultions

Understand differential equations – Euler’s method and RungeKutta method

Understand machine learning related optimization algorithms (gradient descent, stochastic gradient descent, ADAM optimizer etc.)
This course includes:

14 hours ondemand video

13 articles

22 downloadable resources

Full lifetime access

Access on mobile and TV

Certificate of completion

Mathematical background – differential equations, integration and matrix algebra
Course link :https://bit.ly/3G3TzWt
Course size :2.4GB
PASSWORD :getyourcourse.site