
Where to learn the math for data science? - codeitup838364
Hope you all are doing great. I would love to do lower-end kaggle competitions in the coming months. In high school we have not learnt any advanced math yet. I know python well. Where would you recommend to learn the math essentials for starting to learn machine learning&#x2F;data science from like basics?
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logicslave
Don't aimlessly learn math. Even if its from a statistics or machine learning
book. Choose a well regarded model and then study all math that it takes to
understand and use the model well. Learn nothing outside of this. Rinse and
repeat.

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marapuru
I'm currently in a data science traineeship and got this exact advice from my
teachers.

We had to go through the following article two, three, four times and recreate
it ourselves and then explain all the principles to our fellow students. This
helped me a lot in understanding the math behind it.

[https://towardsdatascience.com/netflix-and-chill-
building-a-...](https://towardsdatascience.com/netflix-and-chill-building-a-
recommendation-system-in-excel-c69b33c914f4?gi=97d42649c468)

PS. I'm a sucker when it comes to math. When I got the infamous math questions
with metaphorical elephants on weight scales I couldn't stop but wonder why
the elephant was on the scale.

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ketanmaheshwari
Since you know Python, this book called Think Stats would be a good start, I
think. A free PDF is available here: [https://greenteapress.com/wp/think-
stats-2e/](https://greenteapress.com/wp/think-stats-2e/)

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usgroup
Not sure you need any maths for Kaggle competitions. Learn XGBoost and random
forests for regression and classifications and learn how to evaluate the
models and some of the tricks for coercing different types of data into them
for good peformance. Then curve fit on various data to your heart's content.

It'll take ages before you need maths more complicated than arithmetic.

Taking a stats course won't help you at all. The way canned ML algorithms work
has nothing whatsoever to do with data generating models which is what stats
is about.

You'll start to need maths when it comes to dimensionality reduction and
embedding and that'll come in the form of 95% linear algebra and 5% calculus.
Still no stats though.

Wayyyy down the line if you get into the information theoretic view, or pick
up the fetish of justifying everything via a Bayesian methodology then you'll
need lots of stats.

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needsbetternews
This course from carnegie mellon university has recorded lectures you can
watch and extensive notes which will give you a crash course in the applied
math needed like a linear algebra crash course, probability
[http://www.datasciencecourse.org/lectures/](http://www.datasciencecourse.org/lectures/)
but of course these are just scratching the surface and you can get as
advanced as you want like [https://mml-book.github.io/](https://mml-
book.github.io/)

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s1t5
You need mainly probability, statistics, multivariable calculus and linear
algebra. It's a lot of material to cover and it takes years. I would suggest
to pick a mathematics course/book that's just above your current level
(whatever that is) and start there.

That shouldn't stop you from doing kaggle competitions right now though if
thats' what you like. Personally I dislike kaggle but there's no reason why
you can't just grab a dataset, experiment with different algorithms and learn
as you go.

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pizza
Google around and look for multiple lecturers’s powerpoint pdf slides online
for classes named things like “intro to big data”, “beginning data analysis”,
or something similar. I find that powerpoints are actually a decent way to
learn math if the exposition and explanation of reasoning is half-decent. It’s
good to have multiple examples to cross reference

You may have to learn a little multivariable calculus, or at the very least
look at enough examples of its notation to be able to comfortably skim
equations

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martinmartinez
Mathematics for Machine Learning [https://www.amazon.com/Mathematics-Machine-
Learning-Peter-De...](https://www.amazon.com/Mathematics-Machine-Learning-
Peter-Deisenroth-ebook-
dp-B083M7DBP6/dp/B083M7DBP6/ref=mt_other?_encoding=UTF8&me=&qid=)

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p1esk
Learn about chain rule. Then learn how to multiply two matrices. Look up the
definition of a vector norm. This will cover almost everything you need to
know.

