DataHour: How to tackle Overfitting?

Overfitting is a serious issue in the machine learning  world where a model fits very well in the training data but the performance deteriorates in the test data. The session will cater around different methods to tackle overfitting. 

In this DataHour, the speaker will cover how to reduce overfitting from the data preparation stage (like CSI), what are the different things to look after while selecting cohorts. Then what are the different tips and tricks that can be followed to tackle overfitting in the model building stage like use of regularization, covariate shift analysis ,model ensembling  etc.

Prerequisites:  Enthusiasm to learn Data Science and some preliminary ideas on Machine and Deep Learning along with the basic knowledge on Python and Pythonic platforms like Keras, TensorFlow, PyTorch, Mxnet would be beneficial. 

Subhodeep is a data science professional with 7+ years work experience currently working in Amazon as Data Scientist II. Prior to Amazon he has worked in Citi, ITC Infotech and Rainman Consulting. He has cross domain experience with experience in FMCG/CPG , Retail, HR, BFSI , Reliability spaces.  Academic wise, he has done Masters in Statistics from Calcutta University, Kolkata. You can follow him on Linkedin.