Learning Math for Machine Learning
Here is a nice &quot;cheat sheet&quot; that introduces many math concepts needed for ML: <a href="https:&#x2F;&#x2F;ml-cheatsheet.readthedocs.io&#x2F;en&#x2F;latest&#x2F;" rel="nofollow">https:&#x2F;&#x2F;ml-cheatsheet.readthedocs.io&#x2F;en&#x2F;latest&#x2F;</a><p>&gt; As soft prerequisites, we assume basic comfortability with linear algebra&#x2F;matrix calc [...]
&gt;<p>That&#x27;s a bit of an understatement. I think anyone interested in learning ML should invest the time needed to <i></i>deeply<i></i> 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.<p>Book plug: I wrote the &quot;No Bullshit Guide to Linear Algebra&quot; which is a compact little brick that reviews high school math (for anyone who is &quot;rusty&quot; on the basics), covers all the standard LA topics, and also introduces dozens of applications. Check the extended preview here <a href="https:&#x2F;&#x2F;minireference.com&#x2F;static&#x2F;excerpts&#x2F;noBSguide2LA_preview.pdf" rel="nofollow">https:&#x2F;&#x2F;minireference.com&#x2F;static&#x2F;excerpts&#x2F;noBSguide2LA_previ...</a> and the amazon reviews  <a href="https:&#x2F;&#x2F;www.amazon.com&#x2F;dp&#x2F;0992001021&#x2F;noBSLA#customerReviews" rel="nofollow">https:&#x2F;&#x2F;www.amazon.com&#x2F;dp&#x2F;0992001021&#x2F;noBSLA#customerReviews</a>