Join us for an enlightening webinar on "Introduction to Decision Trees and Random Forests for Practical Data Science," where we will unravel the mysteries of these powerful predictive modeling techniques. This session is designed to provide you with an overview of how decision trees are constructed, optimized, and how they can be amalgamated into a Random Forest for enhanced accuracy and predictive power. Through practical examples and real-world applications, you'll learn how to implement these methods in your data science projects to solve complex problems. Whether you're a beginner in data science or looking to refresh your knowledge, this webinar will equip you with the tools and insights needed to leverage decision trees and random forests effectively.

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Agenda for the session

  • Overview of decision trees
  • Use of Random forest for improved predictive accuracy
  • UT Austin's Data Science & Business Analytics Program
  • Live Q&A

About Speakers

Denver Dias

Data Scientist, Gale Partners, Canada

Denver, a Data Science Leader with 8+ years' experience, crafts advanced Machine Learning models and employs Data Engineering expertise for data-driven decisions. Renowned for high-impact solutions for Fortune 500 clients, leading teams, and cost-saving initiatives. He's revolutionizing Getty Images' A/B Testing platform for algorithm efficacy measurement.

Post Graduate Program in Data Science & Business Analytics

The Post Graduate Program in Data Science and Business Analytics by the McCombs School of Business at the University of Texas at Austin prepares you for a high-growth career in data science and business analytics.

This 6-month program is a comprehensive deep-dive into the world of data science, starting with the basics of data management and Python, followed by advanced classification, regression, and clustering techniques. Working under the guidance of industry experts and the leading faculty from the McCombs School of Business, you will gain hands-on experience with the most relevant business problems in the industry today.

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