Part II – A Whirlwind Tour of Machine Learning Models
In Part I, Best Practices for Picking a Machine Learning Model, we talked about the part art, part science ofContinue Reading
In Part I, Best Practices for Picking a Machine Learning Model, we talked about the part art, part science ofContinue Reading
The part art, part science of picking the perfect machine learning model. The number of shiny models out there canContinue Reading
Training neural networks can be very confusing! What’s a good learning rate? How many hidden layers should your network have?Continue Reading
I’ve recently been having a lot of fun playing with CERN’s Large Hadron Collider datasets, and I thought I’ll shareContinue Reading
Getting started with competitive data science can be quite intimidating. But it’s actually surprisingly simple! I recently started messing aroundContinue Reading
The Goal What’re we doing? We’re going to let XGBoost, LightGBM and Catboost battle it out in 3 rounds: Classification:Continue Reading
Getting started with Kaggle can be an intimidating prospect! So I wrote a kernel to help you get started quickly.Continue Reading
where: job is the gender, writer or engineer data is a collection of the features that comprise a person (hours_worked, salary_in_k, officeContinue Reading
I created Lavanya.ai as a place to use data to tell stories that speak to people, and to take youContinue Reading