In this session, the speaker will showcase and do a code walk through of the End-to-End ML Model Building pipeline by solving a problem.

Here is a broad outline of the session:

Defining Objective (understanding the problem)
Data Requirements & Data Collection (Some insights on how we approach this in the industry when once we identify a problem)
Know & understand your data
Data Preparation (EDA, Cleaning/pre-processing & Feature Engineering)
Fit various ML models + Optimise
Metrics for selection
Deciding the final ML algorithm
Making final predictions
Conclusion by sharing context on how we put it to production and monitor

Machine Learning Problem Solving from Seasoned Professional