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.
About the Author
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.
Sergio’s is Co-author of the book Learning SciPy for Numerical and Scientific Computing - Second Edition (2015) and of the self-published book (in Spanish) Aprendiendo a programar en Python con mi computador: Primeros pasos rumbo a cómputos de gran escala en las Ciencias e Ingenierías, (2016).
The aim of this video is to walk you through some of the applications of Python in Science and Engineering.
The aim of this video is to walk you through the IPython (now Jupyter) Notebook.
The aim of this video is to introduce NumPy arrays as the efficient structure to hold and to perform computations in Python with data.
For and While loops are slow. For some computational tasks they can be avoided. The aim of this video is to take a look at them.
The aim of this video is to learn operating on data with computational efficiency.
The aim of this video is to learn an alternative representation of matrices in NumPy.
This video covers the issue of reading non-binary data from a file and write non-binary data to a file.
The aim of this video is to know more about the tools offered by Python to perform common Scientific and Engineering computations.
The aim of this video is to learn more about the tools that offer Python to perform statistical computations.
The aim of this video is to know what tools offer Python to perform Curve Fitting.
In this video, explore the tools that offer Python the ability to find a numerical solution of ordinary differential equations.
The aim of video is to learn what tools Python offers to evaluate numerically special functions of mathematical Physics and Engineering.
The aim of this video is to explore what tools Python offers to perform 2D data visualization.
The aim of this video is to learn about the tools that Python offers to perform 3D data visualization.
The aim of this video to learn about the scatter and contour plots in Python via Matplotlib.
The aim of this video is to lean what tools are offered by Python to perform histogram plots.
The aim of this video is to learn how to start building machine learning applications in Python.
What we should know to properly handle a data?
The aim of this video is to learn what we should know about data prepossessing nuances.
What should we know about Principal Component Analysis: what is it and on which situations is it used?
The aim of this video is to learn about the two major analysis in Machine Learning.
The aim of this video is to find solutions of optimization problems in Python.
The aim of this video is to use the gradient descent algorithm to find optimal fitting values or a regression problem.
The aim of this video is to learn how to use the gradient descent algorithm in a real data set.
The aim of this video is to learn to use the gradient descent algorithm to tackle some non-linear problems.
The aim of this video is to learn how to approach logistic regression for classification in Python.
The aim of this video is to implement logistic regression for classification in Python.
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