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To access this webinar, please register here: https://hubs.li/Q02FB9GC0

Topic: "Machine Learning Models To Interpretable Rules"

Speaker: Srikanth K S, Director, Data Science at Games24x7 Data Science Professional – A leader with hands-on technical expertise - Data Science, Causal inference, Explainable AI and model interpretability, Predictive modeling, Machine learning, Deep learning, Artificial Intelligence, recommender systems with a background in Applied mathematics, Statistics and Optimization. - At Walmart: Established disciplines as a data science leader, created data science pipelines, built models at scale in Retail areas such as Merchandising, Assortment, Personalization, Advertising platform, Supply-chain, Forecasting and Transportation alongside working with multiple stakeholders, cross-functional teams. Managed a team of data scientists, UI/UX developers, ML engineers and DevOps engineers.

Abstract: Some machine learning models are essentially decision rules with if-then-else constructs. Distillation of this knowledge into rulelists and rulesets provides an interpretable overview of the decision-making process. Explainability leads to clear idea about interventions, explanation to outliers and many more use-cases. We present a few hands-on use cases with 'imodels' (python package for rule based models) and 'tidyrules' (R package for ruleset manipulation and post-hoc reordering and pruning) along with utilities to convert the rulesets into SQL to bring them into production setting.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02zdcSk0 • Code of conduct: https://odsc.com/code-of-conduct/

To access this webinar, please register here: https://hubs.li/Q02FB9GC0

Topic: "Machine Learning Models To Interpretable Rules"

Speaker: Srikanth K S, Director, Data Science at Games24x7 Data Science Professional – A leader with hands-on technical expertise - Data Science, Causal inference, Explainable AI and model interpretability, Predictive modeling, Machine learning, Deep learning, Artificial Intelligence, recommender systems with a background in Applied mathematics, Statistics and Optimization. - At Walmart: Established disciplines as a data science leader, created data science pipelines, built models at scale in Retail areas such as Merchandising, Assortment, Personalization, Advertising platform, Supply-chain, Forecasting and Transportation alongside working with multiple stakeholders, cross-functional teams. Managed a team of data scientists, UI/UX developers, ML engineers and DevOps engineers.

Abstract: Some machine learning models are essentially decision rules with if-then-else constructs. Distillation of this knowledge into rulelists and rulesets provides an interpretable overview of the decision-making process. Explainability leads to clear idea about interventions, explanation to outliers and many more use-cases. We present a few hands-on use cases with 'imodels' (python package for rule based models) and 'tidyrules' (R package for ruleset manipulation and post-hoc reordering and pruning) along with utilities to convert the rulesets into SQL to bring them into production setting.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02zdcSk0 • Code of conduct: https://odsc.com/code-of-conduct/

To access this webinar, please register here: https://hubs.li/Q02FB9GC0

Topic: "Machine Learning Models To Interpretable Rules"

Speaker: Srikanth K S, Director, Data Science at Games24x7 Data Science Professional – A leader with hands-on technical expertise - Data Science, Causal inference, Explainable AI and model interpretability, Predictive modeling, Machine learning, Deep learning, Artificial Intelligence, recommender systems with a background in Applied mathematics, Statistics and Optimization. - At Walmart: Established disciplines as a data science leader, created data science pipelines, built models at scale in Retail areas such as Merchandising, Assortment, Personalization, Advertising platform, Supply-chain, Forecasting and Transportation alongside working with multiple stakeholders, cross-functional teams. Managed a team of data scientists, UI/UX developers, ML engineers and DevOps engineers.

Abstract: Some machine learning models are essentially decision rules with if-then-else constructs. Distillation of this knowledge into rulelists and rulesets provides an interpretable overview of the decision-making process. Explainability leads to clear idea about interventions, explanation to outliers and many more use-cases. We present a few hands-on use cases with 'imodels' (python package for rule based models) and 'tidyrules' (R package for ruleset manipulation and post-hoc reordering and pruning) along with utilities to convert the rulesets into SQL to bring them into production setting.

ODSC Links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://hubs.li/H0Zycsf0 • ODSC blog: https://opendatascience.com/ • Facebook: https://www.facebook.com/OPENDATASCI • Twitter: https://twitter.com/_ODSC & @odsc • LinkedIn: https://www.linkedin.com/company/open-data-science • Slack Channel: https://hubs.li/Q02zdcSk0 • Code of conduct: https://odsc.com/code-of-conduct/