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Back where it all started

How I’d start learning machine learning again (3-years in)

By Daniel Bourke — 10 min read

I’m underground, back where it all started. Sitting at the hidden cafe where I first met Mike. I’d been studying in my bedroom for the past 9-months and decided to step out of the cave. Half of me was concerned about having to pay $19 for breakfast (unless it’s Christmas, driving Uber on the weekends isn’t very lucrative), the other half about whether any of this study I’d been doing online meant anything.

Photo by Hannah Wright on Unsplash

Overview of tokenization algorithms in NLP

By Ane Berasategi — 8 min read

This article is an overview of tokenization algorithms, ranging from word level, character level and subword level tokenization, with emphasis on BPE, Unigram LM, WordPiece and SentencePiece. It is meant to be readable by both experts and beginners alike. If any concept or explanation is unclear, please contact me and I will be happy to clarify whatever is needed. / geralt

What makes a data analyst excellent?

By Cassie Kozyrkov — 6 min read

Before we dissect the nature of analytical excellence, let’s start with a quick summary of three common misconceptions about analytics from Part 1:

  1. Analytics is statistics. (No.)
  2. Analytics is data journalism / marketing / storytelling. (No.)
  3. Analytics is decision-making. (No!)

Data cleaning is finally being automated

By Jeremie Harris — 40 min listen 🎧

It’s cliché to say that data cleaning accounts for 80% of a data scientist’s job, but it’s directionally true.

That’s too bad, because fun things like data exploration, visualization and modelling are the reason most people get into data science. So it’s a good thing that there’s a major push underway in industry to automate data cleaning as much as possible.

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Back where it all started was originally published in on Medium, where people are continuing the conversation by highlighting and responding to this story.