talk-data.com

RSVP and add to your calendar here - https://streamyard.com/watch/bkV8SQeH26C3

This AI course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training and types of machine learning including supervised, and unsupervised learning, as well as some of the most common models such as regression and k-means clustering.

Upon completion, individuals will have a foundational understanding of machine learning and its capabilities and be well-positioned to take advantage of introductory-level hands-on training in machine learning and data science.

Instructor: Sheamus McGovern\, Founder and Engineer \| ODSC AI

Sheamus McGovern is the founder of ODSC AI (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.

*** 🚀 Curious to learn more? Head over to https://odsc.ai/west/bootcamp/ for all the details. Don't forget to use code COMMUNITYWest2025 at checkout for an extra discount! *** ## If you choose to enroll in the 6-Week Virtual AI Bootcamp as a standalone event: Over 6 weeks, gain a comprehensive understanding of AI, from foundational concepts in coding and machine learning to LLMs, AI Agents & RAG - https://aiplus.training/pre-bootcamp/

Some useful links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://aiplus.training/ • ODSC blog: https://opendatascience.com/ • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.ai/code-of-conduct/

RSVP and add to your calendar here - https://streamyard.com/watch/bkV8SQeH26C3

This AI course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training and types of machine learning including supervised, and unsupervised learning, as well as some of the most common models such as regression and k-means clustering.

Upon completion, individuals will have a foundational understanding of machine learning and its capabilities and be well-positioned to take advantage of introductory-level hands-on training in machine learning and data science.

Instructor: Sheamus McGovern\, Founder and Engineer \| ODSC AI

Sheamus McGovern is the founder of ODSC AI (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.

*** 🚀 Curious to learn more? Head over to https://odsc.ai/west/bootcamp/ for all the details. Don't forget to use code COMMUNITYWest2025 at checkout for an extra discount! *** ## If you choose to enroll in the 6-Week Virtual AI Bootcamp as a standalone event: Over 6 weeks, gain a comprehensive understanding of AI, from foundational concepts in coding and machine learning to LLMs, AI Agents & RAG - https://aiplus.training/pre-bootcamp/

Some useful links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://aiplus.training/ • ODSC blog: https://opendatascience.com/ • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.ai/code-of-conduct/

RSVP and add to your calendar here - https://streamyard.com/watch/bkV8SQeH26C3

This AI course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training and types of machine learning including supervised, and unsupervised learning, as well as some of the most common models such as regression and k-means clustering.

Upon completion, individuals will have a foundational understanding of machine learning and its capabilities and be well-positioned to take advantage of introductory-level hands-on training in machine learning and data science.

Instructor: Sheamus McGovern\, Founder and Engineer \| ODSC AI

Sheamus McGovern is the founder of ODSC AI (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.

*** 🚀 Curious to learn more? Head over to https://odsc.ai/west/bootcamp/ for all the details. Don't forget to use code COMMUNITYWest2025 at checkout for an extra discount! *** ## If you choose to enroll in the 6-Week Virtual AI Bootcamp as a standalone event: Over 6 weeks, gain a comprehensive understanding of AI, from foundational concepts in coding and machine learning to LLMs, AI Agents & RAG - https://aiplus.training/pre-bootcamp/

Some useful links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://aiplus.training/ • ODSC blog: https://opendatascience.com/ • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.ai/code-of-conduct/

RSVP and add to your calendar here - https://streamyard.com/watch/bkV8SQeH26C3

This AI course is designed to introduce participants to the basics of artificial intelligence (AI) and machine learning. We will first explore the various types of AI and then progress to understand fundamental concepts such as algorithms, features, and models. We will study the machine learning workflow and how it is used to design, build, and deploy models that can learn from data to make predictions. This will cover model training and types of machine learning including supervised, and unsupervised learning, as well as some of the most common models such as regression and k-means clustering.

Upon completion, individuals will have a foundational understanding of machine learning and its capabilities and be well-positioned to take advantage of introductory-level hands-on training in machine learning and data science.

Instructor: Sheamus McGovern\, Founder and Engineer \| ODSC AI

Sheamus McGovern is the founder of ODSC AI (The Open Data Science Conference). He is also a software architect, data engineer, and AI expert. He started his career in finance by building stock and bond trading systems and risk assessment platforms and has worked for numerous financial institutions and quant hedge funds. Over the last decade, Sheamus has consulted with dozens of companies and startups to build leading-edge data-driven applications in finance, healthcare, eCommerce, and venture capital. He holds degrees from Northeastern University, Boston University, Harvard University, and a CQF in Quantitative Finance.

*** 🚀 Curious to learn more? Head over to https://odsc.ai/west/bootcamp/ for all the details. Don't forget to use code COMMUNITYWest2025 at checkout for an extra discount! *** ## If you choose to enroll in the 6-Week Virtual AI Bootcamp as a standalone event: Over 6 weeks, gain a comprehensive understanding of AI, from foundational concepts in coding and machine learning to LLMs, AI Agents & RAG - https://aiplus.training/pre-bootcamp/

Some useful links: • Get free access to more talks/trainings like this at Ai+ Training platform: https://aiplus.training/ • ODSC blog: https://opendatascience.com/ • Slack Channel: https://hubs.li/Q038cQBy0 • Code of conduct: https://odsc.ai/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/

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/