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Basics of Numerical Methods for Machine Learning & Engg.
Rating: 4.7 out of 5(10 ratings)
669 students

Basics of Numerical Methods for Machine Learning & Engg.

Numerical Methods: Basics of Numerical Analysis for Deep learning, Machine Learning , AI ,Data Science & Engg. students
Created bySandeep Kumar Mathur
Last updated 7/2026
English

What you'll learn

  • Understand how Numerical Methods fits into the broader context of computer science
  • Develop a deep understanding of the concepts of numerical analysis
  • Learn how to interpret formulae and understand practical approach
  • Learn how to deal with common issues in numerical methods

Explore related topics

Course content

8 sections • 62 lectures • 6h 46m total length

  • Intoduction and The Forward Differences7:14

    Explore the fundamentals of numerical analysis, focusing on the calculus of finite differences, forward differences, delta operators, and building a forward difference table.

  • Forward Difference Table4:37

    Explore how to construct a forward difference table, compute delta f(a), delta^2 f(a), and understand triangular and diagonal representations using f(x) values.

  • Illustration16:42

    Illustration1 shows computing y = x^3 + 5x - 7 for x = -1 to 5 and building the forward difference table, highlighting delta y and delta three six.

  • The backward Differences2:00
  • Illustration13:59

    Construct a backward difference table from the given data, listing x from zero to five with the corresponding y values, starting with F0 equals three.

  • Properties of Difference Operator6:16
  • Illustration12:42

    Demonstrates how to compute delta of sin(ax+b) using the definition delta f = f(x+h) − f(x) and the sine subtraction formula, yielding delta sin(ax+b) = 2 cos(ax+b + h/2) sin(h/2).

  • Illustration23:24

    Factorize f(x) = (x^2+5)(x+6) as (x+2)(x+3) and apply the forward difference with h=1 to derive delta f(x) = -2/[(x+2)(x+3)(x+4)].

  • Illustration33:58

    Explains the delta of x as f(x+h)−f(x) with x=1, rewrites in terms of sine and cosine, derives sin(A−B) = sin A cos B − cos A sin B.

  • Illustration41:30
  • Illustration54:45
  • Illustration65:34

    Apply the quotient rule to delta 2^x over (x+1)!. Compute delta f and delta g, then substitute x=1 to derive delta [2^x/(x+1)!] = -(x 2^x)/(x+2)!.

Requirements

  • High school knowledge of Math and specially calculus

Description

Numerical methods play a critical role in machine learning, deep learning, artificial intelligence, and data science. These methods are essential for solving complex mathematical problems that are common in these fields.

One of the most important uses of numerical methods in these areas is in the optimization of machine learning models. Optimization is the process of finding the set of model parameters that minimize a given objective function. This process involves complex mathematical calculations that often require numerical methods .

Here, the course is thoughtfully structured and organised. The topics covered are-

The Calculus of Finite Differences

  • The Forward Differences

  • Forward Difference Table

  • The backward Differences

  • Properties of Difference Operator

Interpolation with equal Intervals

  • Assumptions for methods of Interpolation

  • Newton Gregory Method/Formula

  • Newton Gregory Formula for backward Interpolation

Interpolation with unequal Intervals

  • Lagrange's Interpolation Formula

  • Divided Difference Formula

Numerical Differentiation

Numerical Integration

  • General Quadrature Formula

  • Trapezoidal Rule

  • Simpson's One Third (1/3) Rule

  • Simpson's Three Eighths(3/8)Rule

  • Weddle's Rule

Numerical Solution of Algebraic and Transcendental Equation

  • Properties of Algebraic Equations

  • Synthetic Division

  • Derivative of a Polynomial with synthetic division

  • Methods of finding out roots of equation : Graphical Method

  • Bisection Method

  • Regula Falsi Method/False Position Method

  • Iteration Method

  • Newton Raphson Method

Numerical methods are also used in the analysis of large datasets. Data scientists often encounter datasets that are too large to be processed using traditional methods. In these cases, numerical methods such as randomized linear algebra and Monte Carlo simulations can be used to efficiently process the data.

Here , in this course you'll receive support through a Q&A section, and the course is continually updated based on student feedback, with plans to add new topics in the future.

So why wait?

Enroll today and take the first step toward achieving your goals. With the right tools and support, you can make your dreams a reality and achieve the high score you deserve. Don't miss out on this opportunity to excel and boost your confidence.

Who this course is for:

  • Deep learning, Machine Learning Artificial Intelligence and data science students and professionals

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