Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
1 Hour(s) 5 Minute(s)
Language
English
Taught by
Stone River eLearning
Rating
Course Overview
The fundamental package for scientific computing with Python
At the end of this course, you will have a thorough understanding of Numpy' s features and when to use them. Numpy is mainly used in matrix computing. We'll do a number of examples specific to matrix computing, which will allow you to see the various scenarios in which Numpy is helpful. There are a few computational computing libraries available for Python. It's important to know when to choose one over the other. Through rigorous exercises, you'll experience where Numpy is powerful and develop and understanding of the scenarios in which Numpy is most useful.
- Express fully why Numpy should be used
- Ability to install Numpy
- Understanding of how to use Numpy
Course Content
- 2 section(s)
- 12 lecture(s)
- Section 1 NumPy
- Section 2 Bonus Material
What You’ll Learn
- Express fully why Numpy should be used, Ability to install Numpy, Understanding of how to use Numpy
Skills covered in this course
Reviews
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GGeir B Lorentzen
xxx
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MMegha Chakraborty
It was a good learning experience to know the basics of Numpy
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MMaurya Allimuthu
It is good
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SScott Krehbiel
The instructor is clearly experienced with numpy for mathematical/statistical calculations, but this course does not cover using the np.newaxis feature to enable broadcasting different arrays together. As I work on convolutional neural networks, it's very important to be good at doing various acrobatics with the shapes of arrays so that they can be broadcast, and I was hoping that those would be covered. For example: how array_A[:,:,np.newaxis] is different from array_A[np.newaxis,:,:] and how array shape affects numpy's rules for broadcasting.
