About Rahul Agarwal

Rahul Agarwal is a Machine Learning Engineer at Facebook. Previously, he was a data scientist at Walmart Labs India, and worked at several startups. Find him at @MLWhiz on Twitter, or mlwhiz.com

Rahul Agarwal Posts (18)

• The 5 Classification Evaluation Metrics Every Data Scientist Must Know - 16 Oct 2019
  
  This post is about various evaluation metrics and how and when to use them.
• 6 bits of advice for Data Scientists - 25 Sep 2019
  
  As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.
• Automate Hyperparameter Tuning for Your Models - 20 Sep 2019
  
  When we create our machine learning models, a common task that falls on us is how to tune them. So that brings us to the quintessential question: Can we automate this process?
• The 5 Sampling Algorithms every Data Scientist need to know - 18 Sep 2019
  
  Algorithms are at the core of data science and sampling is a critical technical that can make or break a project. Learn more about the most common sampling techniques used, so you can select the best approach while working with your data.
• The 5 Graph Algorithms That Data Scientists Should Know - 10 Sep 2019
  
  In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.
• The Hitchhiker’s Guide to Feature Extraction - 03 Jun 2019
  
  Check out this collection of tricks and code for Kaggle and everyday work.
• What my first Silver Medal taught me about Text Classification and Kaggle in general? - 13 May 2019
  
  A first-hand account of ideas tried by a competitor at the recent kaggle competition 'Quora Insincere questions classification', with a brief summary of some of the other winning solutions.
• Good Feature Building Techniques and Tricks for Kaggle - 31 Dec 2018
  
  A selection of top tips to obtain great results on Kaggle leaderboards, including useful code examples showing how best to use Latitude and Longitude features.