In this video, we introduce random number generation in NumPy and explain how it is used to simulate data, support statistical analysis, and power many machine learning and modeling workflows. You’ll learn how NumPy generates random values, how to control randomness for reproducibility, and how to work with common probability distributions.
The examples focus on practical use cases such as data simulation, sampling, and experimentation.
Topics covered:
Overview of NumPy’s random module
Generating random numbers from uniform and normal distributions
Creating random arrays with specified shapes
Setting random seeds for reproducibility
Sampling without replacement
Common applications in data analysis and modeling
📌 Recommended: Watch NumPy Array Creation and NumPy Array Access & Reshape before this video for full context.
📂 Code examples and learning materials are available on GitHub:
https://github.com/NaserNikandish/Pyt...
Naser Nikandish
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