
Machine Learning Books That Helped Me Level Up - strikingloo
http://www.datastuff.tech/data-science/3-machine-learning-books-that-helped-me-level-up-as-a-data-scientist/
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Tycho
I think the data scientist hiring frenzy may soon collapse as large companies
run out of patience with their recently formed data science departments that
struggle to deliver ROI (for many reasons, often not the fault of the data
scientists). But I think we are not yet at the final iteration of the job
market for these sorts of skills. Companies usually don't really want someone
who specializes in model tuning and algorithm creation, they want something
like a "full-stack data analyst" \- someone who acknowledges that the modeling
may be 2% of the effort and the rest is business analysis, data wrangling,
engineering, stakeholder management, building tools for users/operators, etc.,
and rolls up their sleeves to deliver an end-to-end solution. There does not
yet exist a catchy name for this role, but I bet that in a few years it will
be what everyone wants to hire. So skate to where the puck will be...

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Maro
Great comment. What you say matches my experience, but not entirely. My take:

The 2% is a bit low as an avg. for ML work; I would say modeling/ML work
ranges from 0 to 20% across the "data team". Most of the work is still data
wrangling/engineering.

I agree that most companies need "full stack data guys". At my current
company, there were 2-3 iterations of previous DS teams who had zero impact
and left behind nothing in production. Then I came in, and being a full stack
guy, I built up a DWH, ETL, dashboards [1] and eventually found opportunities
to put ML into production [2].

I don't agree that the hype will end. The reason = companies/CEOs aren't
rational and don't know what you said above. They'll just keep hiring DS
people/teams and hope that eventually some magic gift pops out. I think this
will keep on going for at least 5 years. I get pinged about 1-2 times per week
by recruiters for Head of DS type positions in London/Dubai, everybody wants
to build data science teams.

[1] [http://bytepawn.com/fetchr-data-science-
infra.html](http://bytepawn.com/fetchr-data-science-infra.html)

[2] [http://bytepawn.com/automating-a-call-center-with-machine-
le...](http://bytepawn.com/automating-a-call-center-with-machine-
learning.html)

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spectramax
Ian Goodfellow’s Deep Learning book pretty much useless. I own it and have
read through most parts of it. I couldn’t explain it better than top Amazon
reviews:

[https://www.amazon.com/Deep-Learning-Adaptive-Computation-
Ma...](https://www.amazon.com/Deep-Learning-Adaptive-Computation-
Machine/product-reviews/0262035618)

And I’m surprised to not find Aurelion Geron’s absolute masterpiece listed
below. I believe it is the best machine learning book _ever_ , although
Statistical Learning mentioned in the article is really good as well :

[https://www.amazon.com/Hands-Machine-Learning-Scikit-
Learn-T...](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-
TensorFlow/dp/1491962291/)

~~~
whymauri
This is pretty harsh. I use Goodfellow as a reference text and then supplement
the mathematics behind it with more comprehensive texts like Hastie or
Wasserman. Maybe if you sit down and read it cover-to-cover, it will seem
disjointed. But I usually read chapters independently - I recently read the
convolutional neural network chapter in preparation for an interview and I
thought it was fine.

~~~
platz
So it's a reference, not a pedagogical tool then?

A reference implies you already know the topic and just want an index to jog
your memory for things you can't hold all in your head at once.

That is different than a pedagogical tool. If so, you shouldn't recommend it
to those want to learn the topic.

~~~
whymauri
Phrased like this, I agree with you. I didn't think about it like that.

I agree with the poster below. Outside of classes, lecture notes, the books I
listed, and Sutton/Barto (Intro. to Reinforcement Learning) have taught me the
material. I use Goodfellow to brush up before interviews or jog my memory
about topic I don't work with very often (like computer vision).

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remilouf
I really recommend Murphy’s “Machine Learning: a probabilistic perspective”.
Murphy’s lays the groundwork for understanding how the algorithms work, why
and how they could be adapted to the problem you’re dealing with. It takes you
from complete beginner (with a reasonable math level) to one step above
`import sklearn as sk`.

The other books I read make the field look like a bunch of heuristics that
just happen to work.

~~~
physicsyogi
Murphy is a nice book, but I’ve always felt like it was more of a survey text
and not one made for diving deep into a given subject. For instance, if you
want to go from theory to writing code, Murphy isn’t necessarily the best book
for that.

~~~
remilouf
I think it was intended to show you the landscape, give you enough tools and
background knowledge so you can go and explore the literature by yourself.
Years after it’s publication it still does a really good job at it.

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deng
Since the site mentions "An Introduction to Statistical Learning":

The first book on statistical learning by Hastie, Tibshirani and Friedman,
which is absolutely terrific, is freely available for download:

The Elements of Statistical Learning

[http://web.stanford.edu/~hastie/ElemStatLearn/](http://web.stanford.edu/~hastie/ElemStatLearn/)

~~~
hooloovoo_zoo
ISL is also free. [http://www-bcf.usc.edu/~gareth/ISL/](http://www-
bcf.usc.edu/~gareth/ISL/)

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dchuk
Lists like this are awesome, but I can’t help but think we need some sort of
lists tool that lets people create them, others vote on them reddit style,
leave comments, rate each item, etc. almost like a subreddit type thing per
list, maybe without the temporal decay component of the algorithm.

Then we could have “10 best intro to machine learning resources” as a living
breathing list.

~~~
trashE
Pls someone do this. I despise Amazons stranglehold of product reviews, esp.
books. Sometimes half of the ratings are on the condition of arrival.

~~~
dchuk
Not to mention amazon isn’t always going to be the only source for resources.
Could be a mix of blog posts, video courses, research papers...anything really

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nafizh
The Introduction to Statistical Learning book is great.

But, and I think this is not stated enough, there is a big difference between
statistical learning and machine learning in terms of how you approach a
problem. The subject matter might be same, but the approach to solve problem
is different, one is a 'statistics' approach, one is a 'CS' approach.
Depending on your background, you might like one but not the other.

You can know more of what I am talking about by reading this famous piece from
Leo Breiman [0].

Personally, I feel I was fortunate enough to learn ML from a so called 'CS'
perspective through Andrew Ng's course on Coursera.

0\.
[https://projecteuclid.org/download/pdf_1/euclid.ss/100921372...](https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726)

~~~
anthony_doan
I agreed, also I agree that the pro and con of each one isn't stated enough
either.

I recently attended Dr. Frank Harrell's workshop and he really put the
differences between the two from his experiences in perspective.

He advocate more for continuous variable responses than what most ML does. He
also put into context what ML does well where the "signal to noise" ratio is
so that the noise are little and signal are more. But when the noises are
getting bigger statistical model will do better.

His examples is the titanic dataset. Paraphrasing but he stated, "I don't care
who live or die." The better question is what is their tendency to live or
die? Instead of classifying it into two categories why not give a percentage?
What if a person is 49% likely to live? Aren't you playing God? And it is up
to the person that's using the model to decide not the modeler. Another case
is that it's a loss of information by forcing it into a classification
problem.

He went on how ML tend to do this force classification. And how it is very
good at things that have low signal to noise, such as game of Go, speech rec,
visual rec, etc...

It's an interesting contrast than Dr. Leo Breiman's The Two Cultures paper. At
least that's my take away, Dr. Breiman may be less opinionated? Both are very
critical of statistic/data models but Dr. Frank Harrell have good remedies and
counter points.

~~~
nafizh
Personally, I don't agree with a lot of Dr. Harell's definitions for SL and ML
to begin with. He mostly talks about clinical trials, a field I don't have
much knowledge about. But a lot of the arguments seem to attack a strawman
defined as ML.

~~~
anthony_doan
Oh I can see that.

I think personally they're just pro and con for both. They're enough problems
out there for both of them to coexist.

With that said, I think the future of AI is going to be a hybrid of SL/ML as
seen on m4 time series competition. ML may be just a stepping stone, like the
two AI winter with expert system dying out. Or perhaps ML is going to just
advance like NN did and went deep learning. Dr. Leo Breiman is correct in
harshly criticizing SL but I believe both sides are going to have to take
harsh objective criticisms for them to move forward.

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3jckd
I wouldn't say these are level-up but rather some introductory material that
covers the basics.

Swapping Introduction to Statistical Learning for Elements of Statistical
Learning is a good step-up if you don't need as much hand-holding (it's
essentially the same book, by the same author just more thorough). Then,
adding Bishop's ML book is a good idea. Although also introductory, it covers
a lot more topics (some kernel methods and probabilistic stuff) and in a more
disciplined way.

Also, while not that popular in the deep learning hype era, Vapnik's Nature of
Statistical Learning is a great read.

~~~
strikingloo
Bishop's looks like an awesome book, and it's on my reading list after many
colleagues recommended it to me. I wouldn't have added it to the list though,
because I haven't read it yet.

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scottlegrand2
I find watching all of the machine learning courses that are posted to YouTube
to be a good way to keep up and to get insight into the thinking of the
authors of recent papers. It has more or less become my morning ritual to
watch one lecture a day.

That said, the past few weeks have been an absolute tsunami of potentially
groundbreaking papers. And it is hard to keep up with The cutting edge.

~~~
selimthegrim
Would you care to list some of them for us?

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master_yoda_1
One god-fatherly advice: you won't get a data science job by just reading
these three books. You need to work hard and do other things too. Like working
on many projects.

~~~
strikingloo
I agree! Personally, I'd advice anyone who starts reading these books to also
practice everything they read on them, and maybe keep all of those projects on
GitHub for potential employers to see them.

