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?
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