just start with the Feynman undergraduate lectures (listed). The easy-mode of that is "Six Easy Pieces" (read it on kindle) which are the easiest 6 lectures.

At some point you'll need math, I recommend https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0... (I actually started here), and for calculus, "No BS Guide to Math/Physics" by the same author. These books both include a review of high school math (i.e. trig) which i needed. For DiffEq I currently recommend Logan's "A First Course in Differential Equations", this is where I am now and I found this the most gentle after trying several textbooks recommended from r/math. Context: I am an adult with an engineering degree from 20 yrs ago.

i read your book cover to cover it was amazing

edit: https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

If they have showed any interest in math and/or physics, you should consider getting them my MATH&PHYS book: https://www.amazon.com/dp/0992001005/noBSmathphys it's very popular for programmers.

I also have a book on linear algebra, which would be good for people doing more machine learning or data sciency stuff: https://www.amazon.com/dp/0992001021/noBSLA

Both books are perfect for math haters, since they start out with a review of high school math.

Depending on your background and intentions, you might like:

Savov: https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

Strang: https://www.amazon.com/Linear-Algebra-Learning-Gilbert-Stran...

Klein: https://www.amazon.com/Coding-Matrix-Algebra-Applications-Co...

I think the pacing and exercises in the above Strang book are great.

There is the No bullshit guide to linear algebra by Ivan Savov.

https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

Could have been us — we have a "free PDF if you email me proof of purchase for print" policy for all our books[1,2,3]. Currently, I handle this manually via email, but I wish this was more automated somehow (e.g. shopping cart plugged into print-on-demand fulfillment API + digital delivery of eBook in all formats).

[1] https://www.amazon.com/dp/0992001005/noBSmathphys (high school math review, mechanics, and calculus) [2] https://www.amazon.com/dp/0992001021/noBSLA (linear algebra) [3] https://www.amazon.com/dp/099200103X/noBSmath (high school math review)

If you're looking for something very basic (high school and calculus), you can check my book No Bullshit Guide to Math & Physics: https://www.amazon.com/dp/0992001005/ Extended preview here: https://minireference.com/static/excerpts/noBSguide_v5_previ...

There is also the No Bullshit Guide to Linear Algebra https://www.amazon.com/dp/0992001021/ Extended preview: https://minireference.com/static/excerpts/noBSguide2LA_previ...

Both come with a review of high school math topics, which may or may not be useful for you, depending on how well you remember the material. Many of the university-level books will assume you know the high school math concepts super well.

One last thing, I highly recommend you try out SymPy which is a computer algebra system that can do a lot of arithmetic and symbolic math operations for you, e.g. simplify expressions, factor polynomials, solve equations, etc. You can try it out without installing anything here https://live.sympy.org/ and this is a short tutorial that explains the basic commands https://minireference.com/static/tutorials/sympy_tutorial.pd...

Speaking of learning/reviewing linear algebra, I wrote the NO BULLSHIT guide to LINEAR ALGEBRA which covers all the material from first year in a very concise manner.

preview: https://minireference.com/static/excerpts/noBSguide2LA_previ... condensed 4 page tutorial: https://minireference.com/static/tutorials/linear_algebra_in... reviews on amazon: https://www.amazon.com/dp/0992001021/noBSLA

Here is a nice "cheat sheet" that introduces many math concepts needed for ML: https://ml-cheatsheet.readthedocs.io/en/latest/

> As soft prerequisites, we assume basic comfortability with linear algebra/matrix calc [...] >

That's a bit of an understatement. I think anyone interested in learning ML should invest the time needed to deeply understand Linear Algebra: vectors, linear transformations, representations, vector spaces, matrix methods, etc. Linear algebra knowledge and intuition is key to all things ML, probably even more important than calculus.

Book plug: I wrote the "No Bullshit Guide to Linear Algebra" which is a compact little brick that reviews high school math (for anyone who is "rusty" on the basics), covers all the standard LA topics, and also introduces dozens of applications. Check the extended preview here https://minireference.com/static/excerpts/noBSguide2LA_previ... and the amazon reviews https://www.amazon.com/dp/0992001021/noBSLA#customerReviews

Here is a shameless plug to my book on linear algebra that comes with an introduction to quantum mechanics (Chapter 9): https://www.amazon.com/dp/0992001021/noBSLA

If you know you linear algebra well, learning quantum mechanics is not so complicated, see the book preview here: https://minireference.com/static/excerpts/noBSguide2LA_previ...

"Pay the authors" is a really good strategy to incentivize the production of quality content. Get rid of the publishers and just have a short supply chain: author --print_on_demand--> readers. With a price tag in the 20-50 range, a prof could make a living from this book, even if the book isn't popular. When using print-on-demand and cutting out all the middlemen, the margins are very good (50% of list price vs 5% if going with mainstream publisher).

The useful part of a publisher is developmental editing (product) and copy editing (Q/A), so there is an opportunity for "lightweight" publishing companies that help expert authors produce the book—like self publishing, but you don't have to do the boring parts. I'm working in that space. We have two textbooks out: https://www.amazon.com/dp/0992001005/noBSmathphys and https://www.amazon.com/dp/0992001021/noBSLA

I'm curious what you think of the "No Bullshit Guide to Linear Algebra" [1]? I'm considering buying it to refresh my knowledge from school. Or what books do you suggest?

[1] https://www.amazon.com/No-bullshit-guide-linear-algebra/dp/0...

Very well done. Short, yet covers all the necessary details.

Shameless plug, check out my book https://www.amazon.com/dp/0992001021/noBSLA for an in-depth view of the linear algebra background necessary for quantum computing.

If you know linear algebra well, then quantum mechanics and quantum computing is nothing fancy: just an area of applications (See Chapter 9 on QM). Here is an excerpt: https://minireference.com/static/excerpts/noBSguide2LA_previ...