Parul Pandey
Machine Learning Engineer at Weights & Biases
Expertise: Data science, machine learning, artificial intelligence
Education: National Institute of Technology Hamirpur
Parul Pandey is a machine learning engineer at Weights & Biases, a developer-first MLOps platform. Before this, she worked as a data scientist at H2O.ai, combining data science and developer advocacy in her work. She is also a Kaggle Grandmaster in the notebooks category and was one of LinkedIn’s Top Voices in the software development category in 2019.
Pandey is co-authoring a book on responsible AI and has written multiple articles focused on data science and software development for various publications. She also has a background in electrical engineering.
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Vector norms are an important concept to machine learning. This guide breaks down the idea behind the L¹, L², L∞, and the Lᵖ norms.
Sorting data is easy with the Pandas library. This tutorial introduces you to the basics.
Trying and failing to decipher your own codebase? Remember: Good code is its own best documentation.
Here are a few important caveats to keep in mind when you’re encoding data with pandas.get_dummies().
Here’s how to heat up your heat maps and make sure they stand out.
Read data directly from the clipboard without saving it first. Here’s how.
Come with me on a whirlwind tour of 5 libraries that will be a great addition to your data science stack.
This open-source Python development framework can help you quantify and eradicate unconscious bias in job descriptions.
Get notified when your long-running cell finishes execution, and get on with your life.
Here’s an overview of some commonly used Python libraries that provide an easy and intuitive way to transform images.