
Introduction to Statistical Learning - noch
http://faculty.marshall.usc.edu/gareth-james/ISL/
======
dnquark
Hastie and Tibshirani teach a free course based on this book on Stanford's
OpenEdX ([https://online.stanford.edu/courses/sohs-ystatslearning-
stat...](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-
learning)). I highly recommend taking this course or reading the book before
delving into ESL. IMO, ESL is excellent as a reference, but trying to learn by
reading it linearly is not an optimal time investment.

Now if only a similar course existed for Wasserman's "All of Statistics..."

~~~
dewy
There's a Youtube playlist[1] of recorded lecture videos by Wasserman from his
CMU course that uses All of Statistics as a textbook.

I haven't watched more than a couple of mins of them (yet), so no idea how
good they are (but the blackboard is quite hard to see in the recordings).
However, it obviously doesn't have all the extra stuff that you would get in a
proper MOOC.

[1]
[https://www.youtube.com/playlist?list=PLJPW8OTey_OZk6K_9QLpg...](https://www.youtube.com/playlist?list=PLJPW8OTey_OZk6K_9QLpguoPg_Ip3GkW_)

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ryankupyn
I'm a big fan of ISL - one of the best intro machine-learning oriented
textbooks out there IMO. If you're looking for book that still offers a broad
survey while going a bit deeper into the math, I recommend Elements of
Statistical Learning as well (they share 2 authors):

[https://web.stanford.edu/~hastie/Papers/ESLII.pdf](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)

~~~
_fullpint
ESL is really really good. Absolutely cannot be recommended enough. I’ve
worked through at few times and it’s a great book to have on the shelf.

ISl is good but as it being an introduction book — the lack of higher order
math wasn’t my favorite.

~~~
abhgh
Recommendation for ESL seconded. One of the best ML books in terms of writing,
development of intuition, breadth of topics (ofc doesn't cover everything, esp
deep learning).

What I love about the book is how the topics are "connected" so to speak. The
narrative within a theme is typically "let's look at problem P, here's
technique Q to solve P, but if you thought about P slightly differently you
would see something like technique R would also work, so let's talk about that
now".

The level of math might be tough for a beginner though.

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disposedtrolley
The authors also published a set of very good lectures covering content in the
book: [https://www.r-bloggers.com/in-depth-introduction-to-
machine-...](https://www.r-bloggers.com/in-depth-introduction-to-machine-
learning-in-15-hours-of-expert-videos/)

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johannes_ne
I'm currently working through this book. Highly recommend, even if you have no
intention on learning R. The R part is very limited, you will not learn R
programming, but if you already know R, it is very useful to end each chapter
with a practical demonstration of the theory.

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lukeplato
I am trying to decide between going through a statistical learning vs. a deep
learning textbook/course. Any thoughts on what would be more rewarding for
someone with no immediate plans to work in ML nor do graduate level research.
Thank you.

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lukejduncan
This is one of my all time favorite technical books. I wrote a review of sorts
a few years back[0]. It doesn’t cover any deep learning topics, which perhaps
dates it at this point, but it gives solid fundamentals on a breadth of
techniques common in industry. This is always in my recommendation list for
folks making the transition from more systems or product engineering to ML.

[0] [https://www.linkedin.com/pulse/introduction-statistical-
lear...](https://www.linkedin.com/pulse/introduction-statistical-learning-
book-luke-duncan)

~~~
lukeplato
Noticed your comment after posting mine:
[https://news.ycombinator.com/item?id=24056852](https://news.ycombinator.com/item?id=24056852)

I was wondering if in retrospect you would have preferred reading the
Goodfellow deep learning book vs. this?

~~~
disgruntledphd2
I've read both, and I would strongly recommend ISL first as it's much broader
and covers the basics much much better than the Goodfellow book.

~~~
lukejduncan
Agreed. I’ve read both and would also recommend this first. The good fellow
book goes deep fast. This was my review at the time
[https://www.goodreads.com/review/show/2196621333](https://www.goodreads.com/review/show/2196621333)

That said, if your choice is more general, statistical learning vs deep
learning, I’m sure at this point you can find more approachable deep learning
primers. This book just isn’t it IMHO.

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saeranv
I've been working my way through this book, and it's fantastic. I love the way
this book grounds all the discussion of statistical learning with a practical
data analysis problem.

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geff82
Is there a book of similar quality that uses Python?

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pks016
Great book. My go-to stats book. It was one of my first intro. book for
statistics with R in undergrad days.

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larrydag
Great resource if one is wanting to learn R.

~~~
noch
> [I]f one is wanting to learn R

A useful practice, in my experience, is to implement R code samples in some
other language, like C/D. Implementing lower level math functions for oneself
can also be fun instead of relying on a library, depending on one's ultimate
goals/interests.

~~~
bvrstvr
I'm a data scientist without a formal background in programming. Can someone
please explain why implementing math functions in C/D is different than doing
it in R?

For example, I would assume that creating a mean function using numbers and
operators would be language-agnostic.

~~~
fizixer
\- R: the mean function is already created

\- C: You will create the mean function

It's the difference between using a calculator and knowing how to multiply two
5 digit numbers with pen and paper.

You might say, "Oh but we can do that in R". Well, if you start doing that one
function at a time, very soon you find yourself with R grinding to a halt.

~~~
cinntaile
If you want to you can recreate the mean function in R as well, so why would
you choose to do it in another language if your goal is to learn R?

~~~
dunefox
No good reason for C/D/... other than the wanting to learn the particular
language. Otherwise, use R or Julia to stay in the scientific domain.

