We could easily overfit machine learning with the data we have, but the quest to reduce the generalization errors, have pushed the Machine Learning researchers to implement new algorithms, and one of the criterion in Supervised Machine Learning, the balance of data distributions is important, but most of the real life datasets are imbalanced, and this gives the zeal to study new algorithms to drive business and research forward. There are various methods at data level and at algorithm level that solves this problem, we will discuss both of them and try to implement both the methods, in this hands-on session.

Prerequisites:

Knowledge of basic supervised machine learning algorithms, Python programming language and basic understanding of data-set would make easier for you to understand the delivering content.

As we are going to perform hands-on, the ones going to run Online, we'll be using Colab. Costcla pre-installed will be good to have for that.

For Offliners, we will be using Jupyter notebook for visualization, pre-installed tflearn, sklearn, imblearn, pandas, numpy, matplotlib, seaborn, costcla, xgboost, geoip (optional) will be helpful.

we will use python3 and pycharm in general.

Additionally, interested ones to perform the hands-on, do join with your fully charged laptops with battery backup, Internet on your own and a can-do attitude!

Speaker:
Tanisha Bhayani:
She is an Associate AI Researcher at F(x) Data Labs Pvt. Ltd. With a strong interest in Research in AI, she tries to implement the research papers she reads, and also implement the new ideas she gets!