#### Categories Machine learning

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>10 hours of interviews for this w/ a dozen or so of top firms in the game. Really grateful to everyone who gave up time & insights, even those that didnt make final cut 🙇♂️ https://t.co/9YOSrl8TdN

For avoidance of doubt, leading tracking analytics firms are now well beyond voronoi diagrams, using more granular measures to assess control and value of space.

This @JaviOnData & @LukeBornn paper from 2018 referenced in the piece demonstrates one method https://t.co/Hx8XTUMpJ5

Bit of this that I nerded out on the most is "ghosting" — technique used by @counterattack9 & co @stats_insights, among others.

Deep learning models predict how specific players — operating w/in specific setups — will move & execute actions. A paper here: https://t.co/9qrKvJ70EN

So many use-cases:

1/ Quickly & automatically spot situations where opponent's defence is abnormally vulnerable. Drill those to death in training.

2/ Swap target player B in for current player A, and simulate. How does target player strengthen/weaken team? In specific situations?

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.

⬇️⬇️⬇️

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city

https://t.co/9aUK1Tclw4

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel

https://t.co/f1joTRaG55

✔️ @dollarsanddata

https://t.co/Mj1owkzRc8

✔️ @awealthofcs

https://t.co/y81KHfh8cn

✔️ @iancassel

https://t.co/KEMTBHa8Qk

✔️ @InvestorAmnesia

https://t.co/zFL3H2dk6s

✔️

Tech focused

✔️ @stratechery

https://t.co/VsNwRStY9C

✔️ @bgurley

https://t.co/NKXGtaB6HQ

✔️ @CBinsights

https://t.co/H77hNp2X5R

✔️ @benedictevans

https://t.co/nyOlasCY1o

✔️

Tech Deep dives

✔️ @StackInvesting

https://t.co/WQ1yBYzT2m

✔️ @hhhypergrowth

https://t.co/kcLKITRLz1

✔️ @Beth_Kindig

https://t.co/CjhLRdP7Rh

✔️ @SeifelCapital

https://t.co/CXXG5PY0xX

✔️ @borrowed_ideas

This thread is for you.

🧵👇

The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.

I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.

Before diving into maths, I suggest first having solid programming skills in Python.

Read this thread for more

These are topics of math you'll have to focus on for machine learning👇

- Trigonometry & Algebra

These are the main pre-requisites for other topics on this list.

(There are other pre-requites but these are the most common)

- Linear Algebra

To manipulate and represent data.

- Calculus

To train and optimize your machine learning model, this is very important.