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Abstract (2-3 lines)
Most of the ML models are limited to finding relationships between variables. In short models are not able to differentiate correlation and causation. Human being's ability to differentiate correlation from causation is the core reason for their intelligence. How can machines scale to that level of intelligence?
Brief Description and Contents to be covered
Causal Models, Probabilistic Graphical Models, Bayesian networks.
Pre-requisites for the talk
Basic knowledge about Modelling.
Time required for the talk
30 Mins.
Link to slides
I have a large set of slides, need to filter and prepare a relevant pitch.
Will you be doing hands-on demo as well?
I will show some model outcomes but not hands-on.
Link to ipython notebook (if any)
About yourself
Data Science Practice Lead, Nagarro.
I spoke at PyData 2019 delhi event.
Are you comfortable if the talk is recorded and uploaded to PyData Delhi's YouTube channel ?
Sure
Any query ?
The text was updated successfully, but these errors were encountered:
Abstract (2-3 lines)
Most of the ML models are limited to finding relationships between variables. In short models are not able to differentiate correlation and causation. Human being's ability to differentiate correlation from causation is the core reason for their intelligence. How can machines scale to that level of intelligence?
Brief Description and Contents to be covered
Causal Models, Probabilistic Graphical Models, Bayesian networks.
Pre-requisites for the talk
Basic knowledge about Modelling.
Time required for the talk
30 Mins.
Link to slides
I have a large set of slides, need to filter and prepare a relevant pitch.
Will you be doing hands-on demo as well?
I will show some model outcomes but not hands-on.
Link to ipython notebook (if any)
About yourself
Data Science Practice Lead, Nagarro.
I spoke at PyData 2019 delhi event.
Are you comfortable if the talk is recorded and uploaded to PyData Delhi's YouTube channel ?
Sure
Any query ?
The text was updated successfully, but these errors were encountered: