Supervised learning - Regression problems

Johnson & Johnson, Vision Care (Ireland)
  • Jan 15 05 PM

  • Online



In this second session in machine learning series, we will study the supervised learning algorithms. We will explore the regression problems and the nature of business problems. Various use cases of the same will also be discussed along with pragmatic real-world implementations.

The session will focus on simple linear regression, multiple linear regression, decision tree, random forest, boosting etc. The concepts of these algorithms will be discussed, the mathematics behind them will be explored and Python implementation will be examined too. The process to select the best algorithm and to measure the accuracy of the solution will also be discussed.
Key points of discussion
  • What is Supervised Learning
  • What are use cases of Supervised learning
  • What is regression analysis
  • Simple linear regression
  • Multiple linear regression
  • Decision Tree
  • Ensemble learning: Random Forest
  • Measure the accuracy of the model
  • Implementation using Python
Vaibhav Verdhan

Principal Data Scientist

Johnson and Johnson Vision Care Ireland

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist.

Event Category :


Important Dates :

15 Jan 21 | 05 PM

Start date

15 Jan 21 | 06 PM

End date