What you would learn in Numerical Methods and Optimization in Python course?
This course focuses on mathematical methods and optimization algorithms within Python, the programming language.
The first part is about linear systems and matrix algebra like matrix multiplication and gaussian elimination and the application of these techniques. We will examine the well-known page rank algorithm developed by Google. PageRank algorithm.
We will discuss numerical integration. Techniques to use such as the trapezoidal rule Simpson formula and the Monte-Carlo technique to determine the definitive integral.
The next chapter will solve differential equations using Euler's method and the Runge-Kutta method. We will examine instances like the pendulum problem and ballistics.
We will also continue to examine optimization methods based on machine learning. The gradient descent stochastic gradient descent algorithm, ADAGrad, RMSProp, and ADAM optimizer will be reviewed the theory and its implementation.
Part 1: Numerical Methods Fundamentals
Basics of numerical methods
efficiency C, Java and Python
Section 2 Linear Algebra and Gaussian Elimination
portfolio optimization through matrix algebra
Section 3 - Eigenvectors and Eigenvalues
eigenvectors and eigenvalues
applications of eigenvectors in machine-learning (PCA)
Google's PageRank algorithm explained
The Lagrange interpolation theory
Implementation and application of interpolation
Section 5 The Root Finding Algorithms
solving non-linear equations
Bisection and Newton's Method
Section 6 Numerical Integration
The rectangle method and the trapezoidal method
7. Section 7 - Differential Equations
solving differential equations
Pendulum problem and ballistics
Section 8 Numerical Optimization (in Machine Learning)
gradient descent algorithm
stochastic gradient descent
ADAGrad as well as RMSProp algorithms
ADAM optimizer explained
Learn about linear systems and Gaussian elimination
Understand eigenvectors and eigenvalues
Learn about Google's PageRank algorithm.
Know the concept of the importance of numerical integration
Know Monte-Carlo simulations
Know differential equations, Euler's method as well as Runge-Kutta's method
Know about machine learning-related algorithmic optimization (gradient descent stochastic gradient descent ADAM optimizer, etc.)
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