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Everything you need to know about the math for machine learning as a beginner.
Before diving into the math, I suggest first having solid programming skills.
For example
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For example
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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
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- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling
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If you want to learn python, these courses are freecodecamp could be of help to you.
Basics: youtube .com/watch?v=rfscVS0vtbw
Intermediate :youtube .com/watch?v=HGOBQPFzWKo
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Basics: youtube .com/watch?v=rfscVS0vtbwIntermediate :youtube .com/watch?v=HGOBQPFzWKo(4 / 17)
You need to have really strong fundamentals in programming, because machine learning involves a lot of it.
It is 100% compulsory.
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It is 100% compulsory.
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Another question that I get asked quite often is when should you start learning the math for machine learning?
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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.
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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.
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For instance I was recently competing in a kaggle challenge.
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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.
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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.
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- Linear Algebra
- Calculus
- Trigonometry
- Algebra
- Statistics
- Probability
Now here are the math 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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> 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(11 / 17)
Seeing Theory
> This website gives you an interactive to learn statistics and probability
seeing-theory. brown. edu/basic-probability/index.html
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> This website gives you an interactive to learn statistics and probability
seeing-theory. brown. edu/basic-probability/index.html(12 / 17)
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
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> 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(13 / 17)
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
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> 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(14 / 17)
Khan Academy
>The resource you must refer to when you forget something or want to revise a topic.
khanacademy. org/math
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>The resource you must refer to when you forget something or want to revise a topic.
khanacademy. org/math(15 / 17)
Essence of calculus
> A beautiful series on calculus, makes everything seem super simple
youtube. com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x
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> A beautiful series on calculus, makes everything seem super simple
youtube. com/watch?v=WUvTyaaNkzM&list=PL0-GT3co4r2wlh6UHTUeQsrf3mlS2lk6x(16 / 17)
The math for Machine learning e-book
> This is a book aimed for someone who knows a decent amount of high school math like trignometry, calculus etc.
I suggest reading this after having the fundamentals down on khan academy.
mml-book. github .io
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> This is a book aimed for someone who knows a decent amount of high school math like trignometry, calculus etc.
I suggest reading this after having the fundamentals down on khan academy.
mml-book. github .io(17 / 17)
