Here's everything you need to know about the math for machine learning.

(+ free resources )
🧵👇

Before diving into the math, I suggest first having solid programming skills.

For example👇

(2 / 18)
In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(3 / 18)
If you want to learn those concepts for python, these courses are freecodecamp could be of help to you.

🔗Basics:youtube∙com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube∙com/watch?v=HGOBQPFzWKo

(4 / 18)
👉You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

(5 / 18)
👉Another question that I get asked quite often is when should you even start learning the math for machine learning?

(6 / 18)
👉Math for machine learning should come after you have worked on a project or two, doesn't have to a complex one at all, but one that gives you a taste of how machine learning works.

(7 / 18)
👉Here's how I do it, I look at the math when I have a need for it.

For instance I was recently competing in a kaggle machine learning challenge.

(8 / 18)
I was brainstorming about which activation function to use in a part of my neural net, I looked up the math behind each activation function and this helped me to choose the right one.

(9 / 18)
The topics of math you'll have to focus on for machine learning
- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability

Now here are the resources and a brief description about them.

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Neural Networks
> A series of videos that go over how neural networks work with approach visual, must watch.

🔗youtube. com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

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Seeing Theory
> This website helps you learn statistics and probability in an intuitive way.

🔗seeing-theory. brown. edu/basic-probability/index.html

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Gilbert Strang's lectures on Linear Algebra (MIT)

> This is 15 years old but still 100% relevant today!
Despite the fact these lectures are made for freshman college students at MIT,I found it very easy to follow👌

🔗youtube. com/playlist?list=PL49CF3715CB9EF31D

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Essence of Linear Algebra
> A beautiful playlist of videos which teach you linear algebra through visualisations in an easy to digest manner

🔗youtube. com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab

(14 / 18)
Khan Academy
>The resource you must refer to when you forget something or want to revise a topic super quick

🔗khanacademy. org/math

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Essence of calculus
> A beautiful series on calculus, makes everything seem super simple.

🔗youtube. com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x

(16 / 18)
The math for Machine learning e-book

> This book is for someone who knows quite a decent amount of high school math like trignometry, calculus, I suggest reading this after having the fundamentals down on khan academy.

mml-book. github .io

(17 / 18)
If you found this thread helpful then don't forget to follow me, it takes a ton of effort to write these threads and your support keeps me going. 🔥🙏

Good luck in your machine learning journey!

(18 / 18 🎉)

More from Pratham Prasoon

More from Machine learning

Happy 2⃣0⃣2⃣1⃣ to all.🎇

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 is a Twitter series on #FoundationsOfML.

❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.

This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵

Last time we talked about how Machine Learning works.

Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric


According to the nature of that experience, we can define different formulations, or flavors, of the learning process.

A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇

1️⃣ Supervised Learning

In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.

👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.

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