— In the News —
A recent McKinsey survey found that although most businesses are struggling to capture real value from their analytics, there's an elite group of companies that are succeeding with analytics at scale. This article identifies the 9 key drivers that separate the best from the rest.
Lots of people have ideas about how to fight fake news but as this interview from the trenches reveals, it's vastly more complicated than most people think.
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— Tools and Techniques —
If you're building for production, your training data is much more important than your models. That's the reverse of what people in academia are used to. In this post, Pete Warden explains why and offers practical suggestions for getting more value from your data.
Causal inference and do-calculus allows you to understand a problem and establish what needs to be estimated from data based on your assumptions captured in a causal diagram. In this introduction, Ferenc Huszár explains how it works and why understanding it should be a fundamental part of your toolkit.
Here's a practical look at how Airbnb uses large-scale deep learning models to help users on both sides of its two-sided marketplace.
A Complete Machine Learning Project Walk-Through in Python
— Data Viz —
Colors are an important part of data visualizations and should be carefully selected. This latest post by Lisa Charlotte Rost walks through the key considerations for when - and how - to use color. Includes a great selection of linked references.
— Career —
A Machine Learning Engineer isn't exactly an engineer. And not exactly a data scientist either. This article explores the role of the Machine Learning Engineer, including day-to-day tasks, skills needed, potential opportunities, potential frustrations, and what it takes to succeed.