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.
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.
Overview of Logistic Regression for Classification and Prediction 06m 36s
Implementing Logistic Regression for Classification via SciPy Functions 07m 43s
Numerical and Scientific Computing with SciPy
3 hours 38 minutes
Sergio Rojas is currently a Full Professor of Physics at the Universidad Simón Bolívar, Venezuela. Regarding his formal studies, he earned in 1991 a B.S in Physics with Thesis on Numerical Relativity from the Universidad de Oriente, Estado Sucre, Venezuela, and then, in 1998, he earned a Ph.D. in Physics from the Physics Department of the City College of the City University of New York, where he worked on the applications of Fluid Dynamics in the flow of fluids in porous media, gaining and developing since then a vast experience in programming as an aid to scientific research via fortran77/90 and C/C++. In 2001, he also earned a Master’s degree in computational finance from The Oregon Graduate Institute of Science and Technology.
Sergio’s teaching activities involve lecturing undergraduate and graduated physics courses at his home university, Universidad Simón Bolívar, Venezuela, including a course on Monte Carlo Methods and other on Computational Finance. His research interests include physics education research, fluid flow in porous media, and the application of the theory of complex systems and statistical mechanics in Financial Engineering. More recently, Sergio has been involved in Machine Learning and its applications in Science and Engineering via the Python programming language.