Towards Data Science

A Medium publication sharing concepts, ideas, and codes.

488,779 191 Followers
Editors
Ludovic Benistant

Editor at Towards Data Science

Amber Teng

A writer, learner, and explorer, Angela Teng spends most of her time thinking about how interdisciplinary collaboration can galvanize innovations in technology.

Anne Bonner

Secretly believes everyone should get involved with tech. Wants to help with that. Let’s connect on LinkedIn! https://www.linkedin.com/in/contentsimplicity/

TDS Editorial Team

Sharing concepts, ideas, and codes.

Elliot Gunn

Data science @ Towards Data Science. Previously @ C4ADS (data science intern), Global Strategy Lab (research fellow). Background in economics & ML.

Linda Chen

Share what I learned, and learn from what I shared. All about machines, humans, and the links between them. Take everything with a grain of salt.

Caitlin Kindig

Editor at Towards Data Science

Jeremie Harris

Co-founder of SharpestMinds, host of the Towards Data Science podcast. ⚛︎ Physics | 🤖 Machine learning | 🤔 Philosophy | 🚀 Startups.

Latest Posts

Reinforcement Learning Made Simple (Part 2): Solution Approaches

A Gentle Overview of RL solutions, and how to categorize them. Important takeaways from the Bellman equation, in Plain English.

Supervised Learning vs Unsupervised Learning

This article will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.

Interesting AI/ML Articles You Should Read This Week (Oct 24)

How China leverages AI to tackle Covid-19, and other interesting AI/ML articles you should read this week.

Regularization. What, Why, When, and How?

In this article, I want to take an in-depth look at regularization.

k-Nearest Neighbors (kNN) for anomaly detection

Bite-sized data science for outlier & anomaly detection

Query and analyze Stripe data in Python

MRR and Churn calculations

Data sources are getting standardized; can analytics, data science, and ML keep up?

A stance on the opportunity for out-of-the-box insight for standard schemas

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