Our guest today is Parul Pandey, Principal Data Scientist at H2O.ai, Kaggle Grandmaster (notebooks) & book author of “Machine Learning for High Risk Applications”.
In our conversation, we first dig into Kaggle. Parul explains how she became a Grandmaster, shares tips about data analysis and discusses the pros of learning on Kaggle.
The second part of the episode is around machine learning for high risk applications. We talk about the risks of using AI to make decisions, talk about interpretable algorithms and give advice on how to deploy robust models.
If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.
Machine Learning for High Risk Applications: https://www.oreilly.com/library/view/machine-learning-for/9781098102425/
Kaggle notebook (gold medal), "Geek Girls Rising : Myth or Reality!": https://www.kaggle.com/code/parulpandey/geek-girls-rising-myth-or-reality/notebook
Blog post on Explainable Boosting Machines: https://towardsdatascience.com/the-explainable-boosting-machine-f24152509ebb
Follow Parul on LinkedIn: https://www.linkedin.com/in/parulpandeyindia/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
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(00:00) : Intro
(01:57) : How Parul got into AI
(07:20) : Kaggle & Becoming a Grandmaster
(13:52) : Advice for good data analysis
(20:27) : Pros of learning on Kaggle
(24:28) : ML for high risk applications
(49:20) : Interpretable algorithms
(55:28) : Deploying robust models
(01:01:22) : Future of AI
(01:05:47) : Career advice
Our guest today is Parul Pandey, Principal Data Scientist at H2O.ai, Kaggle Grandmaster (notebooks) & book author of “Machine Learning for High Risk Applications”.
In our conversation, we first dig into Kaggle. Parul explains how she became a Grandmaster, shares tips about data analysis and discusses the pros of learning on Kaggle.
The second part of the episode is around machine learning for high risk applications. We talk about the risks of using AI to make decisions, talk about interpretable algorithms and give advice on how to deploy robust models.
If you enjoyed the episode, please leave a 5 star review and subscribe to the AI Stories Youtube channel.
Machine Learning for High Risk Applications: https://www.oreilly.com/library/view/machine-learning-for/9781098102425/
Kaggle notebook (gold medal), "Geek Girls Rising : Myth or Reality!": https://www.kaggle.com/code/parulpandey/geek-girls-rising-myth-or-reality/notebook
Blog post on Explainable Boosting Machines: https://towardsdatascience.com/the-explainable-boosting-machine-f24152509ebb
Follow Parul on LinkedIn: https://www.linkedin.com/in/parulpandeyindia/
Follow Neil on LinkedIn: https://www.linkedin.com/in/leiserneil/
————
(00:00) : Intro
(01:57) : How Parul got into AI
(07:20) : Kaggle & Becoming a Grandmaster
(13:52) : Advice for good data analysis
(20:27) : Pros of learning on Kaggle
(24:28) : ML for high risk applications
(49:20) : Interpretable algorithms
(55:28) : Deploying robust models
(01:01:22) : Future of AI
(01:05:47) : Career advice