Basics Of Numerical Methods For Machine Learning & Engg.

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

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

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 DifferencesThe Forward DifferencesForward Difference TableThe backward DifferencesProperties of Difference OperatorInterpolation with equal IntervalsAssumptions for methods of InterpolationNewton Gregory Method/FormulaNewton Gregory Formula for backward InterpolationInterpolation with unequal IntervalsLagrange's Interpolation FormulaDivided Difference FormulaNumerical DifferentiationNumerical IntegrationGeneral Quadrature FormulaTrapezoidal RuleSimpson's One Third (1/3) RuleSimpson's Three Eighths(3/8)RuleWeddle's RuleNumerical Solution of Algebraic and Transcendental EquationProperties of Algebraic EquationsSynthetic DivisionDerivative of a Polynomial with synthetic divisionMethods of finding out roots of equation : Graphical MethodBisection MethodRegula Falsi Method/False Position MethodIteration MethodNewton Raphson MethodNumerical 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.

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