Ask HN: What is your goto resource for learning about big data, ML, AI etc? - vijayr
======
curiousgal
HN has a _great_ and I mean absolutely great search feature via Algolia
[https://hn.algolia.com](https://hn.algolia.com) and this particular question
keeps springing up every now and then, no one seems to use the feature despite
the search bar being at the bottom of every page.

Edit: removed "inb4 downvotes".

~~~
atom_enger
I never knew it was at the bottom and I use this site all the time. Thanks for
pointing that out. However, it does raise some questions about the UI in this
case. Can't we put the search box up in the header where people expect it to
be?

~~~
Mandatum
I only tend to use search now and again, since it's not always used I prefer
it not take up initial screen space. Also I think the majority of HN users are
able to figure out a way of searching this site relatively easy, I see it as a
non-issue.

~~~
teapot01
I think the happy medium would be to add a link to search in the top bar but
not the actual search bar.

------
dhawalhs
For complete newbies (but with programming experience), I would recommend this
UW Coursera course to get introduced to ML Basics:
[https://www.coursera.org/learn/ml-
foundations](https://www.coursera.org/learn/ml-foundations)

Early this year Apple acquired Turi for $200 million. It was founded by Carlos
Guestrin, one of the professors who is teaching the course.

We (Class Central) are also working on a six part Wirecutter style guide to
learning Data Science online. Here is part 1: [https://www.class-
central.com/report/best-programming-course...](https://www.class-
central.com/report/best-programming-courses-data-science/)

Feedback would be appreciated (on the format as well as content)!

~~~
tgokh
I'm a huge fan of the rest of this Coursera specialization (or was, until they
started charging to submit assignments for it mid-specialization, but I
digress...)

Carlos and Emily do a great job diving deeper than most other online courses
into the math behind different algorithms without making the math too
theoretical. I'm a grad student in engineering, so I wanted to understand not
only how to run these algorithms but also how they work and these courses were
great for learning in a mathematically rigorous but still approachable sort of
way.

The only criticism I've heard of this series is that it uses
Turi/Dato/Graphlab instead of SciKit-Learn. I did the courses that exist so
far using GraphLab, but I'm starting to redo the assignments using SciKit now
so that I learn that toolkit as well.

~~~
dhawalhs
I think they start charging after the second course.

I am in the same boat as you. I am currently doing Udacity's Machine Learning
Nanodegree. But I think I would have felt lost if I hadn't done the first two
courses of that Coursera Specialization.

Just started, but it seems that Pandas and SciKit-Learn are very similar to
Dato/Graphlab from a usage perspective.

------
geebee
It depends on your focus, of course. Andrew Ng's coursera is famous, and it's
ideal for someone who wants to get into the mathematics behind various ML
algorithms. However, this class is will take you into implementing algorithms,
but is less about applying them.

If you want to just try them out, I'd honestly recommend just going through
the scikit-learn documentation. Almost all of the algorithms provide an
example, and the API is pretty consistent across different ML algorithms, to
the extent that it can be.

People learn differently, some people prefer to get into the math right away,
others will never be interested in it. I'm interested, but I tend to be more
motivated when I've used the algorithms, start to learn about how and why they
perform well or poorly under various circumstances, and then dig into the
mathematics specifically to find out why.

Also, I'm not going to be creating new ML algorithms. So, you know, that also
influences my level of interest. I do care about the mathematics involved,
because I do want to genuinely understand why some outputs are available for
random forests but not naive bases or logistic regression, why performance
and/or accuracy is great in some circumstances and not others, and I don't
want to have to rely on _too_ much hand waving. But if you want to actually
develop and research novel ML algorithms, you'd need to get considerably
deeper into the math.

------
jotto
similar question was just asked 2 weeks ago:

    
    
        Ask HN: How to get started with machine learning?
    

[https://news.ycombinator.com/item?id=12713056](https://news.ycombinator.com/item?id=12713056)

------
k26dr
The scikit-learn documentation is solid:

[http://scikit-learn.org/stable/](http://scikit-learn.org/stable/)

------
sremani
Udacity has a free Introduction to Machine Learning (which use scikit-learn,
python). They also have nano-degrees which are paid.

------
BrandonBradley
For big data, 'Big Data' by Nathan Marz was an excellent read. The conceptual
chapters are top notch, and the implementation chapters give you a good look
into the tools used for the field at the time of publishing.

------
crystalPalace
I enjoy the way this site is written and its focus on getting developers up
and running quickly while still instilling conceptual basics.

[http://machinelearningmastery.com/linear-algebra-machine-
lea...](http://machinelearningmastery.com/linear-algebra-machine-learning/)

[http://machinelearningmastery.com/machine-learning-for-
progr...](http://machinelearningmastery.com/machine-learning-for-programmers/)

------
dharma1
There are a lot of good online courses to get started, I like the Stanford
CS231n lectures -
[http://www.youtube.com/watch?v=F-g0-6_RRUA&list=PLLvH2FwAQhn...](http://www.youtube.com/watch?v=F-g0-6_RRUA&list=PLLvH2FwAQhnpj1WEB-
jHmPuUeQ8mX-XXG&sns=em)

For keeping up with the latest research, once you know what you are doing,
reading papers on Arxiv daily/weekly is a great way to keep up, nearly
everything gets published there

------
glamp
[http://blog.yhat.com/](http://blog.yhat.com/): Tutorials, example apps, and
other stuff.

------
nborwankar
Shameless plug: LearnDataScience [http://learned.com](http://learned.com) is a
git repo with Jupyter Notebooks, data and instructions. It's meant for
programmers, assumes no math background and addresses data cleaning issues
which most classes ignore. Having said that Andrew Ng's class on Coursera is
gold.

~~~
le_scientifique
I think you meant [http://learnds.com/](http://learnds.com/)

------
kobeya
Ignore the domain but... try this:

[https://www.reddit.com/r/MachineLearning/](https://www.reddit.com/r/MachineLearning/)

It is a _remarkably_ high signal to noise community.

------
eliben
I think it's time for someone to write the equivalent of
[http://norvig.com/21-days.html](http://norvig.com/21-days.html) for ML/big
data :-)

------
samteeeee
Sirajology:
[https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)

~~~
kevinskii
Yikes. I didn't know it was possible to feel embarrassed by watching a
TensorFlow video.

------
imrank1
For a ML intro Coursera's machine learning course
[https://www.coursera.org/learn/machine-
learning](https://www.coursera.org/learn/machine-learning) is great. I have
not been through the entire course but for someone who has no background in
it, its a good intro as the video themselves are solid.

------
smnplk
I know you didn't ask for this, but here is a gentle introduction to ML
[http://www.soc.napier.ac.uk/course-
notes/sml/manual.html](http://www.soc.napier.ac.uk/course-
notes/sml/manual.html) :P

------
raju_bala
Conferences like WWW, KDD, ICML for latest, coursera for basics, and textbooks
like Pattern matching by Bishop.

------
KennSippell
[https://www.kaggle.com/](https://www.kaggle.com/)

------
enthdegree
A classic reference is _Pattern Recognition and Machine Learning_ by Bishop

------
ivan_ah
METACADEMY is pretty good: short summaries + prerequisite graph

[https://metacademy.org/roadmaps/](https://metacademy.org/roadmaps/)

------
Drdrdrq
[http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/)

Excellent book for starting with NN and DL.

------
nonbel
The best way to learn is by doing, imo. Just go join a kaggle competition.
Maybe people know others it is so easy to partake in?

------
cstanley
[http://datasciencemasters.org/](http://datasciencemasters.org/)

------
snambi
Nice. I was looking for ML resources.

