Numerical and Scientific Computing with SciPy
Publisher: Packt Publishing
Final Release Date: March 2017
Run time: 3 hours 38 minutes

Master the capabilties of SciPy and put them to use to solve your numeric and scientific computing problems

About This Video

  • Get to grips with the functionalities offered by the Python SciPy Stack (Numpy, Scipy library, and Matplotlib) to computationally tackle scientific and engineering problems.
  • Utilize various algorithms via the SciPy Stack to solve numerically problems related to linear algebra, data analysis, visualization, and much more,
  • Your one-stop tutorial to master the Python SciPy Stack and write fast, efficient solutions for your numerical computational needs in any field.

In Detail

The SciPy Stack is a collection of Open-Source Python libraries finding their application in many areas of technical and scientific computing. It builds on 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. Accordingly, gaining a solid working knowledge on some of the basic functionality of the SciPy Stack to solve mathematical models numerically is clearly the first step before one can start using it to tackle 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 different functionalities offered by the main modules comprising 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.

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