09 Data Science Interactive: Monte Carlo Simulation

Data Science Interactive Python Demonstrations: Chapter 09: Monte Carlo Simulation
In this walk-through, I explain the basics of Monte Carlo Simulation (MCS). Essential for:
* doing math with distributions to build uncertainty models
* bootstrap for machine learning model bagging and random forest
* Markov chain Monte Carlo for Bayesian regression, etc.
Follow along with the interactive Python code in a Jupyter Notebook available in my GitHub repositories github.com/GeostatsGuy/Python... . For more complete lectures check out my other KZbin lectures with linked Python workflows and demonstrations:
* Monte Carlo Simulation:
• 05b Data Analytics: Mo...
* Bootstrap:
• 08b Data Analytics: Bo...

[@muratkaradag3703]

Thank you Michael for this awesome video!

[@vitorribeirosa]

Thank you very much for sharing this outstanding content.

[@twistedpancake7214]

Man knows his shit

[@user-xc9mo9qh4q]

Happy New Yr, waiting for DS & ML project end-end. kindly make it

[@user-xc9mo9qh4q]

😍 thx for new topic

[@doms962]

Let's say that I'm tracking the profile of some person. I choose features F1, F2, F3, ..., Fn. I designed idiocy function fi. This is a function which maps features in the range between 0 and 100. You say that we need to model the distributions F_x. How I could do it? Do I need to steal this knowledge from somewhere?