Do you want to learn the maths for machine learning but don't know where to start?

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 details👇

https://t.co/sSN3jdxDwK
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.
- Statistics

Make "sense" out of the data you have.

- Probability

Make decisions under uncertainty.
These are some of the resources I recommend for learning these topics 👇
Neural Networks

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

🔗youtu.​be/aircAruvnKk
Seeing Theory
> This website helps you learn statistics and probability in an intuitive way.

🔗seeing-theory.​brown.​edu/basic-probability/index.​html
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
My Thread on Linear Algebra.

https://t.co/3H7U2HJgTd
The 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
Khan Academy
>You'll find a course on everything here! Khan Academy is the first place I'll go to when I want to learn something.

🔗khanacademy.​org/math
Essence of calculus
> A beautiful series on calculus, makes everything seem super simple.

🔗youtube.​com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x
The math for Machine learning e-book

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

🔗mml-book.​github.​io

More from Pratham Prasoon

More from Machine learning

10 machine learning YouTube videos.

On libraries, algorithms, and tools.

(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)

🧵👇

1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.

Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.

https://t.co/HqE9yt8TkB


2⃣ The Pandas library is the gold-standard to manipulate structured data.

Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."

https://t.co/aOLh0dcGF5


3⃣ Data visualization is key for anyone practicing machine learning.

Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.

https://t.co/QxjsODI1HB


4⃣ Another trendy data visualization library is Seaborn.

@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.

https://t.co/eAU5NBucbm

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