The math for machine learning always scared me.

Until...

This year when I across these free resources which helped me in a massive way!

Here's everything you need to know about math for machine learning and resources that you can learn from.

(I wish I had this before)

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

For example

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

If you want to learn these 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

You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

Another question that I get asked quite often is when do should you even start learning the math for machine learning?

Math for machine learning should come after you have worked on some projects, doesn't have to a complex one at all, but one that gives you a taste of how machine learning works in the real world.

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 challenge.

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.

Typically I would recommend one to get really good at data cleaning too, but I came across @trymito which was a game changer.

It an excel like interface for processing data without entering a single command!

You can use it freely from this link http://bit.ly/trymito 

One more thing before we look into the resources, I highly recommend that you take this course, it goes over machine learning without any of the math, this will get you more comfortable with machine learning.

Machine learning foundations course
youtube∙com/watch?v=_Z9TRANg4c0

The topics of math you'll have to focus on
- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability

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

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

Seeing Theory
> This website gives you an interactive to learn statistics and probability

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

Gilbert Strang lectures on Linear Algebra (MIT)
> They're 15 years old but still 100% relevant today!
Despite the fact these lectures are for freshman college students ,I found it very easy to follow

youtube. com/playlist?list=PL49CF3715CB9EF31D

Essence of Linear Algebra
> A beautifully crafted set 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
>The resource you must refer to when you forget something or want to revise a topic super quick

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 is a book aimed 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

I really hope this thread could make your machine learning journey just a little bit easier.

Good luck with all of your future endeavours in machine learning!