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
If you want to learn these concepts for python, these courses are freecodecamp could be of help to you.Basics:youtube∙com/watch?v=rfscVS0vtbwIntermediate :youtube∙com/watch?v=HGOBQPFzWKo
If you want to learn these concepts for python, these courses are freecodecamp could be of help to you.Basics:youtube∙com/watch?v=rfscVS0vtbwIntermediate :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!
You can follow @PrasoonPratham.
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