Dec
2018 Jan Webcasts & Webinars
All (108) | Courses, Education (11) | Meetings (8) | News, Features (11) | Opinions, Interviews (31) | Top Stories, Tweets (11) | Tutorials, Overviews (30) | Webcasts & Webinars (6)
- Webinar: AI, Machine Learning and Chatbots Improving Insurance Profitability & CX, Feb 15
- Jan 29, 2018.
Join Insurance Nexus as we talk to MetLife, Chubb and Nationwide about how to prioritize investments and internal resources. Learn which innovations will have the biggest impact on customer experience and improved profitability.
- Operational Best Practices for Enterprise Data Science
- Jan 24, 2018.
Join Team Anaconda for a live webinar, Jan 30, 2pm CT, as we tackle the four main concerns we hear from our customers and show you best practices for managing enterprise data science: scalability, security, integration, and governance.
- Webinar: Minimizing Model Risk with Automated Machine Learning, Jan 31
- Jan 17, 2018.
See how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements.
- Webcasts: Finding analytic solutions to real problems
- Jan 3, 2018.
The Technically Speaking webcast series provides real-word case studies with key insights on overcoming the challenges in data collection, preparation, and analysis - find the webcast that fits your current challenge.
- Enhancing Anti-Money Laundering Programs with Automated Machine Learning, Jan 11 Webinar
- Jan 3, 2018.
In this webinar, Jan 11, DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.
- Data Science in 30 Minutes: A Conversation with Gregory Piatetsky-Shapiro, President of KDnuggets
- Jan 3, 2018.
KDnuggets founder, Gregory Piatetsky-Shapiro, joins Michael Li, CEO and founder of The Data Incubator, Jan 11 at 2:30 pm PT/ 5:30 pm ET for their monthly webinar series, Data Science in 30 Minutes. Gregory will discuss his career, from AI to Data Mining to KDD to Data Science and back to AI, and examine current trends in the field.
