Join us for a live webinar on "Introduction to Supervised Learning and Regression," tailored for professionals and enthusiasts looking to build a strong foundation in machine learning. This session will walk you through the fundamentals of supervised learning, with a deep dive into regression techniques and their real-world applications. Explore how regression helps solve practical business problems. Whether you're new to ML or expanding your knowledge, this webinar offers a practical start to mastering supervised learning.

Webinar Registration

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Agenda for the session

  • Understand Supervised Learning and how it differs from Unsupervised Learning
  • Get introduced to Regression techniques
  • Explore real-world use cases of Regression
  • Live Q&A

About Speakers

Mr. Anuj Saboo

Data Science Mentor at Great Learning

Anuj Saboo is a data science leader with strong expertise in AI, machine learning, and business intelligence. With a proven track record in deploying predictive models and transforming raw data into strategic insights, he brings both technical depth and real-world application. In this session, Anuj will introduce supervised learning and regression—explaining key concepts, algorithms, and how to build and interpret regression models to inform data-driven decisions.


MIT IDSS's Data Science and Machine Learning: Making Data-Driven Decisions Program

The Data Science and Machine Learning: Making Data-Driven Decisions Program has a curriculum carefully crafted by MIT faculty to provide you with the skills & knowledge to apply data science techniques to help you make data-driven decisions. This data science program has been designed for the needs of data professionals looking to grow their careers and enhance their data science skills to solve complex business problems. In a relatively short period of time, the program aims to build your understanding of most industry-relevant technologies today such as machine learning, deep learning, network analytics, recommendation systems, graph neural networks, and time series.