Add to basket or enquire
Master the capabilities of SciPy and put them to use to solve your numeric and scientific computing problems.
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.
What You Will Learn:
- Get to know the benefits of using the combination of the Python SciPy Stack (NumPy, Scipy, and Matplotlib) as a programming environment for technical and scientific purposes
- The use of the SciPy Stack in general applications of Engineering and scientific numerical problem solving.
- The use of the SciPy Stack for solving fundamental basic Machine Learning models.
- Create and manipulate Numpy array objects to perform numerical computations fast and efficiently.
- Use of the Scipy library to compute eigenvalues and eigenvectors and apply it to Principal Component Analysis
- Make use of the SciPy Stack to collect, organize, analyze, and interpret data.
- Analize linear and non-linear regression problems via gradient descent.
Who is this course for?
Style and Approach:
This course mainly focuses on the implementation of the SciPy concepts using real-word examples.
A comprehensive coverage of concepts in SciPy is coupled with examples of varying difficulty levels, to ensure you are ready to solve any kind of problem.
The course is designed in such a way that you won’t have to refer to any other documentation or resource.
Questions and answers
Currently there are no Q&As for this course. Be the first to ask a question.
Currently there are no reviews for this course. Be the first to leave a review.