HACKER Q&A
📣 rory_isAdonk

Resources to teach myself Statistics?


All are welcome, what seems to be in short supply is stuff at either end, the fundamentals and what a master student might encounter.

To briefly explain why I'm asking this, and why the answers may be of value to others in this community:

I'm trying to break into AI, to understand a large amount of the theory you effectively have to be well versed in statistics. I come from an engineering background where it's really more understanding abstract concepts at speed and applying them correctly with frameworks. While I understand a lot can be achieved by going through projects in TensorFlow and doing some Googling, I feel like I don't understand the internals. Thanks for taking the time to read this.

  👤 extremelearning Accepted Answer ✓
"๐—˜๐—น๐—ฒ๐—บ๐—ฒ๐—ป๐˜๐˜€ ๐—ผ๐—ณ ๐—ฆ๐˜๐—ฎ๐˜๐—ถ๐˜€๐˜๐—ถ๐—ฐ๐—ฎ๐—น ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด" ๐—ฏ๐˜† ๐—›๐—ฎ๐˜€๐˜๐—ถ๐—ฒ, ๐—ง๐—ถ๐—ฏ๐˜€๐—ต๐—ถ๐—ฟ๐—ฎ๐—ป๐—ถ, & ๐—™๐—ฟ๐—ถ๐—ฒ๐—ฑ๐—บ๐—ฎ๐—ป

This is one of the clearest and most respected statistics books ever written.

I personally owe the start of my machine learning career to this book!

You will find so many people around the world (online and IRL) who consider this the bible for statistical learning.

It is so readable, and yet filled with gems and insights from one of the world's most preeminent statisticians.

Free PDF: http://ow.ly/v5Uw50obzpO

Datasets & code: http://ow.ly/a8UZ50obzpN

R Code: http://ow.ly/EEdf50kXUBS

Videos: also available at various sources.

👤 mindcrime
For a "Stats 101" experience:

https://www.youtube.com/playlist?list=PL5102DFDC6790F3D0

For Probability Theory:

https://www.youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFg...

https://www.youtube.com/playlist?list=PL2SOU6wwxB0uwwH80KTQ6...

👤 hrokr
I agree you'll definitely want to read Elements of Statistical Learning but there are a few more, namely Think Stats and Think Bayes.

Since no one has really said much about Bayes yet, I think it worth mentioning just how useful it is in DS and ML. A Bayesian approach makes a very good baseline and often one that is hard to beat.

If you're not particular fluent with Probability and Statistics now, let me suggest you add in Khan Academy (make sure to pick the CLEP version) and JBstatistics. Khan has the advantage of quizzes (so you're not just kidding yourself that you know the material). JBstatistics has the advantage of really good explanations. You'll probably want to watch Khan at x1.5 speed.

👤 TheGrkIntrprtr
Ian Goodfellow has a nice free book that includes an "applied math and machine learning basics" section that gives a nice overview of linear algebra, probability theory etc that make it a nice starting point. It's also specifically written for software engineers I believe: https://www.deeplearningbook.org/
👤 ystad
I think your question is I want to understand the math behind all of this: I would start by doing Andrew Ng's courses on ml and DL at Coursera

Math is wide for AI and moves into multiple disciplines such as calculus, linear algebra.

Once you have done the above courses you can dive accordingly http://sgsa.berkeley.edu/current_students/books/

👤 beisner
โ€œAll of Statisticsโ€ by Larry Wasserman is a very good - if concise - introduction to statistics. Doesnโ€™t require too much background, although the problems can get pretty hairy. Unfortunately, many of the proofs in the field are based on โ€œtricksโ€ that accumulate, which makes the learning curve steeper for people who donโ€™t have as much background in finding/using these tricks.
👤 nicdc
This is a easy, short and classic read: https://www.google.com/books/edition/Cartoon_Guide_to_Statis...

EDIT: Also very basic. Should have added that.

👤 nisachar
Manga guide to Statistics