| 001 Euler's Method - Concept.en.srt | 15.9 KB | ||
| 001 Euler's Method - Concept.mp4 | 18.6 MB | ||
| 001 Gauss Elimination - Concept.en.srt | 14.9 KB | ||
| 001 Gauss Elimination - Concept.mp4 | 39.4 MB | ||
| 001 Introduction to Optimization.en.srt | 12.3 KB | ||
| 001 Introduction to Optimization.mp4 | 40.2 MB | ||
| 001 Introduction to Roots of Equations.en.srt | 4.8 KB | ||
| 001 Introduction to Roots of Equations.mp4 | 10.3 MB | ||
| 001 Linear Regression - Concept.en.srt | 15.9 KB | ||
| 001 Linear Regression - Concept.mp4 | 57.3 MB | ||
| 001 Trapezoidal Rule - Concept.en.srt | 14.3 KB | ||
| 001 Trapezoidal Rule - Concept.mp4 | 43.4 MB | ||
| 002 Analytical Solutions.en.srt | 18.7 KB | ||
| 002 Analytical Solutions.mp4 | 77.9 MB | ||
| 002 Bisection Method - Concept.en.srt | 8.8 KB | ||
| 002 Bisection Method - Concept.mp4 | 8.8 MB | ||
| 002 Euler's Method - Python Code.en.srt | 19.4 KB | ||
| 002 Euler's Method - Python Code.mp4 | 74.8 MB | ||
| 002 Gauss Elimination - Python Code.en.srt | 25.4 KB | ||
| 002 Gauss Elimination - Python Code.mp4 | 98 MB | ||
| 002 Linear Regression - Python Code.en.srt | 19.1 KB | ||
| 002 Linear Regression - Python Code.mp4 | 80.9 MB | ||
| 002 Trapezoidal Rule - Python Code.en.srt | 16.3 KB | ||
| 002 Trapezoidal Rule - Python Code.mp4 | 61.6 MB | ||
| 003 Bisection Method - Python Code.en.srt | 17 KB | ||
| 003 Bisection Method - Python Code.mp4 | 57.5 MB | ||
| 003 Gauss Elimination Code for Any Size System.en.srt | 10.1 KB | ||
| 003 Gauss Elimination Code for Any Size System.mp4 | 38.5 MB | ||
| 003 Heun's, Midpoint & Ralson Methods - Concept.en.srt | 13.7 KB | ||
| 003 Heun's, Midpoint & Ralson Methods - Concept.mp4 | 46.9 MB | ||
| 003 Newton Raphson to Find Optimums - Concept.en.srt | 4.5 KB | ||
| 003 Newton Raphson to Find Optimums - Concept.mp4 | 4.9 MB | ||
| 003 Polynomial Regression - Concept.en.srt | 10.3 KB | ||
| 003 Polynomial Regression - Concept.mp4 | 39.9 MB | ||
| 003 Simpson's 1_3 Rule - Concept.en.srt | 13.8 KB | ||
| 003 Simpson's 1_3 Rule - Concept.mp4 | 41.9 MB | ||
| 004 False Position Method - Concept.en.srt | 9.2 KB | ||
| 004 False Position Method - Concept.mp4 | 25.3 MB | ||
| 004 Heun's, Midpoint & Ralson Methods - Python Code.en.srt | 19.1 KB | ||
| 004 Heun's, Midpoint & Ralson Methods - Python Code.mp4 | 88 MB | ||
| 004 Newton Raphson to Find Optimums - Python Code.en.srt | 8.1 KB | ||
| 004 Newton Raphson to Find Optimums - Python Code.mp4 | 23.9 MB | ||
| 004 Polynomial Regression Using Gauss Elimination - Python Code.en.srt | 21.9 KB | ||
| 004 Polynomial Regression Using Gauss Elimination - Python Code.mp4 | 102.7 MB | ||
| 004 Simpson's 1_3 Rule - Python Code.en.srt | 14.4 KB | ||
| 004 Simpson's 1_3 Rule - Python Code.mp4 | 71.3 MB | ||
| 005 False Position Method - Python Code.en.srt | 10.9 KB | ||
| 005 False Position Method - Python Code.mp4 | 52.7 MB | ||
| 005 Golden Section Search - Concept.en.srt | 14.2 KB | ||
| 005 Golden Section Search - Concept.mp4 | 49.4 MB | ||
| 005 Polynomial Regression Using NumPy- Python Code.en.srt | 4.7 KB | ||
| 005 Polynomial Regression Using NumPy- Python Code.mp4 | 24.5 MB | ||
| 005 Romberg Integration - Concept.en.srt | 18.2 KB | ||
| 005 Romberg Integration - Concept.mp4 | 53.4 MB | ||
| 005 Runge Kutta Methods (3rd & 4th Order) - Concept.en.srt | 10.4 KB | ||
| 005 Runge Kutta Methods (3rd & 4th Order) - Concept.mp4 | 42 MB | ||
| 006 Golden Section Search - Python Code.en.srt | 18.1 KB | ||
| 006 Golden Section Search - Python Code.mp4 | 67.2 MB | ||
| 006 Newton Raphson - Concept.en.srt | 6.8 KB | ||
| 006 Newton Raphson - Concept.mp4 | 19.3 MB | ||
| 006 Romberg Integration - Python Code.en.srt | 21.8 KB | ||
| 006 Romberg Integration - Python Code.mp4 | 101.9 MB | ||
| 006 Runge Kutta Methods (3rd & 4th Order) - Python Code.en.srt | 14.8 KB | ||
| 006 Runge Kutta Methods (3rd & 4th Order) - Python Code.mp4 | 58.9 MB | ||
| 007 Applying ALL Runge Kutta Methods on Various Examples.en.srt | 14.1 KB | ||
| 007 Applying ALL Runge Kutta Methods on Various Examples.mp4 | 74.5 MB | ||
| 007 Newton Raphson - Python Code.en.srt | 11.9 KB | ||
| 007 Newton Raphson - Python Code.mp4 | 47.6 MB | ||
| 008 Secant Method - Concept.en.srt | 10.2 KB | ||
| 008 Secant Method - Concept.mp4 | 31.8 MB | ||
| 009 Secant Method - Python Code.en.srt | 9 KB | ||
| 009 Secant Method - Python Code.mp4 | 41.4 MB | ||
| 010 Multiple Root - Concept.en.srt | 7.5 KB | ||
| 010 Multiple Root - Concept.mp4 | 17.9 MB | ||
| 011 Multiple Root - Python Code.en.srt | 12.8 KB | ||
| 011 Multiple Root - Python Code.mp4 | 49.3 MB | ||
| Bonus Resources.txt | 307.2 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 78 total files | |||
Numerical Methods in Python Programming
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 38 lectures (6h 4m) | Size: 1.54 GB
Learn the workings of the most common numerical methods and a step by step process on how to program each of them
What you'll learn:
Approximate integrals using Trapezoidal rule, Simpson's 1/3 rule and Romberg integration
Find roots of equations using bisection, False position, newton Raphson and secant methods
Find analytically the optimum min and max of a function
Solve Ordinary differential Equations using Runge Kutta Methods (i.e. Euler, Heun's, Midpoint and Ralston Methods in addition to fourth order Runge Kutta Method
Find numerically the optimum min and max using Golden section Search method, newton Raphson Technique and finally the gradient decent/ascent method
Solve Systems of Equations using Gauss elimination
Perform curve fitting using regression analysis including linear and polynomial regression in addition to linearization for fitting more complex functions
Requirements
Computer & Access to Microsoft Excel
Knowledge of basic Algebra, Geometry & Calculus Concepts
Knowledge of basic Python Programming
Description
Numerical modeling is a very powerful branch of mathematics. It is capable to solve very complex problems using very simple techniques.
It is a branch that can differentiate and integral without the need to use any of the sometimes complex differentiation and integration rules. It can create best fit models with just knowing a data set. It can create functions where the only thing we know is its derivative and a condition. And best of all, it can generate approximations that have such a low percentage error that they are as good as the true value.
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 1.4 GB | freecoursewb | 7 months | 9 | 0 | |
|
Udemy - Basic Numerical Methods - Numerical Analysis for M - c Learning Posted by
freecoursewb in Other
|
3 GB | freecoursewb | 3 years | 0 | 0 |
| 3.7 GB | freecoursewb | 3 years | 0 | 0 | |
| 3.4 GB | freecoursewb | 4 years | 0 | 0 | |
| 1.4 GB | freecoursewb | 4 years | 5 | 0 |
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