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The fastest way to diagnose your statistical alignment

Why these cat pics? On the left, it’s all about perspective. On the right, it’s all about quantities that don’t move around. But mostly, I needed something to shield your eye from the spoilers below until you’ve seen the video.

Summary


You know machine learning is off to a rocky start when…

Adapted from Wikipedia.

“Among the machine learning strategy consultations you’ve done, which kinds of product team were the most challenging to work with?”

  1. They’re marketing victims with unrealistic expectations
    * Special case: Willing to launch at all costs
    * Special case: No data (and other basic requirements)
  2. There’s a lack of respect for skills diversity
    * Special case: Toxic snobbery
  3. The team has no idea who’s in charge
    * Special…


Too good to fail? The surprising way a top-performing system can hurt you

  • Chris Careless is a constant disappointment to you, performing your task well 70% of the time and producing an absolute cringe the rest of the time. Watching Chris make 10 attempts is more than enough to provoke an “oh, dear” response from you.
  • Ronnie Reliable is another story. You’ve seen Ronnie in action over a hundred times and you’ve been consistently impressed.

This isn’t about bad projects


Essential psychology for all data professionals

Image: SOURCE


Take a “moment” to explore some fundamentals

Get your distribution basics in Part 1 if you’re new to this space. Image: SOURCE.

Mean

Expected value


Back-to-basics on data science fundamentals

Random variable

A random variable is…


Getting Started

Know your species of machine learning task

Before we dive deeper into supervised learning, in this video I give you a quick refresher on how that differs from unsupervised learning.

Basics: Algorithm vs Model


There is no classification.

There is no classification… and regression is something else entirely. Meme template from The Matrix.

Discrete versus continuous

  • Continuous data (measured, not counted), e.g. 173.5…


Continuous, discrete, categorical, cardinal, sequential… keep going!

Data types in statistics and analytics

Don’t worry, it’s not this complicated of a data taxonomy. Some of these critters look wonderfully derpy. Image: SOURCE.
  • Continuous data (measured, not counted), e.g. 176.5 cm (my height), 12% (free space on my phone), 3.141592… (pi), -40.00 (where Celsius meets Fahrenheit), etc.
  • Discrete data (counted…

Cassie Kozyrkov

Head of Decision Intelligence, Google. ❤️ Stats, ML/AI, data, puns, art, theatre, decision science. All views are my own. twitter.com/quaesita

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