DataHour: Boosting Performance with Ensemble Methods

Ensemble methods are a powerful approach in machine learning that involves combining multiple models to improve overall predictive accuracy and generalization.

In this webinar, Vikas will provide an introduction to ensemble methods and discuss some of the most popular techniques, such as bagging, boosting, and stacking. He will also explore the benefits and drawbacks of ensemble methods, and how they can be applied in a variety of real-world scenarios, such as classification, regression, and anomaly detection. By the end of this webinar, you will have a better understanding of ensemble methods and how they can be leveraged to enhance the performance of your machine-learning models.

Prerequisites: The zeal for learning new technologies, and good to have a basic knowledge of Machine Learning. 

In today's data-driven world, the role of a data scientist has become increasingly important for organizations looking to gain insights and make informed decisions. Meet K Vikas, an NIT Rourkela alumni and a Staff Data Scientist at Avlino, who has made significant contributions to the field of Machine Learning and Operations Research. With a background in Mathematics, Vikas has spent the last 5 years exploring various aspects of machine learning including classical ML, deep learning (CV/NLP), and big data.