Numerical and Scientific Computing with SciPy

The SciPy Stack is a collection of Open Source Python libraries finding their application in many areas of technical and scientific computing. It builds the capabilities of the NumPy array object for faster computations, and contains modules and libraries for linear algebra, signal and image processing, visualization, and much more. Therefore, getting a solid work knowledge on some of the basic functionality of the SciPy Stack to solve mathematical models digitally is clearly the first step before one can begin to use it to deal with large-scale computational projects either in the industry or in the academic world.

This practical course begins with an introduction to the Python SciPy Stack and a coverage of its basic usage cases. You will then delve right into the various functionalities offered by the main modules, including the SciPy Stack (Numpy, Scipy, and Matplotlib) and see the basics on how they can be implemented in real-life scenarios. You will see how you can make the most of the algorithms in the SciPy Stack to solve problems in linear algebra, numerical analysis, visualization, and much more, including some practical examples drawn from the field of Machine Learning. By the end of this course, you will have all the knowledge you need to take your understanding of the SciPy Stack to a new level altogether, and tackle the trickiest problems in numerical and scientific computational programming with ease and confidence.