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Comment by hackernewsacct | Hacker News Reader

What are good online sources for a beginner to learn calculus, linear algebra and stats/probability needed for machine learning?

I don't want courses that teach math by coding but actual math courses. A lot of these online ML courses treat math as a black box so you never fully understand what's going on & a lot of math based ML courses are too advanced.

So I'd rather focus on understanding the math basics before diving into ML. So what do you guys recommend?

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Linear Algebra by Gilbert Strang, MIT https://www.youtube.com/watch?v=ZK3O402wf1c

Probability by Joe Blitzstein, Harvard http://projects.iq.harvard.edu/stat110/youtube

Excellent, thanks for you recommendation!

Gilbert Strang is an excellent lecturer and teacher. He, along with Andrew Ng, are amongst the best.

Calculus, by James Stewart. I recommend you buy a dead tree copy of an old edition (I paid $5 for mine, shipped. I don't know how they made money off that sale.) Might not be the best book, but it covers everything you need to know, has good exercises, everyone uses it, you can find the solutions manual online, and the harder problems have an answer on stack overflow.

And some linear algebra book by Strang (as others have said.) I don't remember which one I learned from, but I do remember it was excellent.

I can't recommend a good stats/probability book. People recommend Statistical Inference by Casella and Berger, although I wasn't super impressed with it.

You'll also want a good understanding of set theory and proof writing. I don't remember what book I learned from, but try to find some introduction to math thought.

This is all assuming you have a good understanding of algebra. The above mentioned books require geometry/trig, but you don't actually need that for machine learning.

> Calculus, by James Stewart

I learned from the Stewart book and it's not terrible, but I don't think that is a very good book for self learning. How to ace calculus or the calculus lifesaver, plus a book with solved examples is probably better for self study.

This was my university calculus textbook and I liked it well enough. Granted, I was learning with the assistance of lectures to teach me the concepts too so I don't know how you'd fare with only this textbook, but I liked it well enough to drop coin on a physical copy.

My philosophy with textbooks was to buy only ones I felt I'd go back to after uni - basic references in each major field I studied - and this was reasonable for me for calculus. My only regret was shelling out for new copies because I was young and stupid and thought I needed them right now for the class, before I knew better. You can certainly pick up cheap second-hand copies per the parent post.

I also used Kreyzig too but found that to be a bit harder to learn from, albeit more in depth.

I'd also recommend Calculus for the Practical Man. It's what Feynman used and is quite good for learning how to calculate various things with calculus.

Incidentally, my college calculus class also used Stewart.

For linear algebra this youtube playlist called essence of linear algebra is probably the best intro I've ever seen. it's relatively short (like 2 hours) but gives you all the right intuitions

https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...

Google "MIT OCW Scholar".

Sites like Khan Academy, PatrickJMT, Paul's Online Math Notes, and BetterExplained are useful, too.

Coursera and edx also have offerings.

Khan Academy is an amazing true MOOC resource. The calculus goes up to early undergrad. The differential calculus is the most important for ML, in my oppinion.

Probability and statistics will help ML as well.

I would recommend Khan Academy before any other resources, whatever your level. It has online exercises with auto-correction, and a mastering system that uses spaced repetition. This will ensure you'll actually assimilate the material. After you can use another resource as a reference.

Why not a book instead like the elements of statistical learning?

http://statweb.stanford.edu/~tibs/ElemStatLearn/download.htm...

Based on Amazon reviews recommending this book to a beginner is like recommending rudin to a high school junior that wants to learn calculus for the first time. Is there a better book?

Introduction to Statistical Learning? It's free and has an available MOOC http://www-bcf.usc.edu/~gareth/ISL/

Thanks for the link.

https://www.amazon.com/Elements-Statistical-Learning-Predict...

https://www.amazon.com/Introduction-Statistical-Learning-App...

The former seems to assume prior knowledge and the latter seems more beginner friendly, except it uses R (in case anyone wants the distinction).

These books assume some degree of Linear Algebra, ESL is definitely not for a entry level.

I would like to add www.codingthe matrix.com for any developers, which has a MOOC as well (Coursera), as a complement to Strang.

R is well worth learning if you're going to be doing any serious work with data. It's a weird-ass language, but it does data analysis/graphing/modelling really, really well.

I support this statement. Not a book for beginners, IMO

This is in no way appropriate for someone who is asking for math basics.

A lot of the material in this book makes much more sense having had experience coding machine learning algorithms.

Ocw and Gilbert strangs material is a must for linear algebra. For beginners, I enjoyed Stewart calculus.

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