# Basic Numerical Methods for Machine Learning Engg. students

Numerical Methods: Basics of Numerical Analysis for Deep learning, Machine Learning , AI ,Data Science & Engg. students

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.

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.

Course Content

• The Calculus of Finite Differences –> 12 lectures • 53min.
• Interpolation with equal Intervals –> 12 lectures • 1hr 15min.
• Interpolation with unequal Intervals –> 6 lectures • 33min.
• Numerical Integration –> 5 lectures • 13min.
• Numerical Solution of Algebraic and Transcendental Equation –> 14 lectures • 1hr 37min. Requirements

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.

Here, following topics of Numerical Methods are dealt in detail:

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 Integration

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

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 such as gradient descent, Newton’s method, and conjugate gradient methods.

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.

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