
Ask HN: Best online courses for machine learning? - aliirz
Ask HN: What are some of the best online resources to jump into practical machine learning
======
sidkhanooja
Contrary to popular opinion, I find that Andrew Ng's Intro to ML (on
Coursera/Stanford) focuses too much on basic math and theory - which doesn't
detract from the course's quality, but makes the course a drudgery to go
through.

Programming exercises involve a single line or two, and that too in Octave -
which was all the rage back when the course was launched, but it's not so
useful now.

Instead, start with this - [https://www.fast.ai](https://www.fast.ai)

It emphasizes practicality to the extreme - you are only taught theory/domain
knowledge when needed. The instructor's amazing, the massive scale of
knowledge imparted boggles the mind, and you feel like you've accomplished
something when you're finished with it.

Best of all, it's free. And you can start Deep Learning there too when you're
done with ML, if you feel the need (or interest).

~~~
k4ch0w
I'd second this class. I have done both and if you're brand new to machine
learning I'd do fast.ai first. You gain a greater understanding from Andrew's
course, but if you're not going to become a practitioner fast.ai is better.

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charlysl
To learn a solid theoretical foundation: Caltech's "Learning from data" [1].
It is one of those rare courses where the professor is so good that he manages
to make tricky concepts seem almost trivial (like Paar's "Understanding
cryptography").

Look elsewhere if all you want is to learn tools and start practical projects
ASAP without really understanding what you are doing. Tools come and go and
will serve you for a while, concepts are timeless and will serve you for life.
You need both, of course, but I wouldn't skip the theory, specially when such
an amazing course is on tap.

[1]
[https://work.caltech.edu/telecourse.html](https://work.caltech.edu/telecourse.html)

~~~
ultrasounder
Agreed! 100%. What level of math is required for the LFD course?

~~~
charlysl
Don't worry about the math, one of the great things about this course is that
the professor is a magician when it comes to explaining and defanging scary
looking math, you'll be fine. Having said that, it's mostly basic matrix and
vector algebra, basic probability and basic calculus (all high school level).

~~~
ultrasounder
Thanks for the words of encouragement! With that said I will watch the
lectures, read the book and do the assignments.

------
luhego
I would recommend this course: [https://mlcourse.ai/](https://mlcourse.ai/).
It starts on February 11.

PROS

\- It has a good balance between theory and practice.

\- There are lectures covering the theory and practice.

\- There are practical assignments you need to code with Python.

\- It includes in-class Kaggle competitions.

\- It includes a rating system so you can compare your progress with other
students.

CONS

\- It has some prerequisites. You need to know Python(at a basic level) and
some basic knowledge of math(calculus, linear algebra, etc).

\- It is a difficult course. You will need between 5 to 10 hours each week for
assignments. Each week is usually harder than the previous one.

You can find more details in this post:
[https://www.kaggle.com/general/77771](https://www.kaggle.com/general/77771)

------
Isamu
Google ML crash course: [https://developers.google.com/machine-learning/crash-
course/](https://developers.google.com/machine-learning/crash-course/)

Facebook field guide to machine learning: [https://research.fb.com/the-
facebook-field-guide-to-machine-...](https://research.fb.com/the-facebook-
field-guide-to-machine-learning-video-series/)

Training on Machine Learning with AWS:
[https://aws.amazon.com/training/learning-paths/machine-
learn...](https://aws.amazon.com/training/learning-paths/machine-learning/)

~~~
simple_phrases
Can anyone comment on whether these are worth the time investment versus more
traditional courses?

~~~
ultrasounder
I haven't taken any of these courses. There were these courses from Amazon
[https://aws.amazon.com/training/learning-paths/machine-
learn...](https://aws.amazon.com/training/learning-paths/machine-
learning/data-scientist/) that were highly recommended. But it all comes down
to what you really want. If you just want to get a bird's eye view of the ML
algorithms buffet for you to implement one of their Black box models then they
are a good place to start. But for anything deeper, I would go with Charlysl's
recommendation.

------
nwsm
Udemy's Machine Learning A-Z course:
[https://www.udemy.com/machinelearning/](https://www.udemy.com/machinelearning/)

All lessons in R and Python. Wide range of content.

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notoriousjpg
This'd be a safe bet [https://www.coursera.org/learn/machine-
learning](https://www.coursera.org/learn/machine-learning)

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briga
The online Stanford courses--CS224, CS229, CS231--are an excellent
introduction into modern AI. CS231 with Andrej Karpathy in particular was a
game-changer for me. It has three very thorough and well-designed assignments
that will have you implement many of the basic algorithms discussed in the
course.

The pre-requisite linear algebra for these subjects can be learned through
Gilbert Strang's MIT course. A basic grounding in statistics and probability
theory, along with calculus will also help.

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solomatov
This one:
[https://see.stanford.edu/course/cs229](https://see.stanford.edu/course/cs229)

It's a recording of CS229 at Stanford. This course is much harder and more
thorough than the one on coursera.

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UsernameTaken5
There's a free intro course here: [https://www.udemy.com/machine-learning-
intro-for-python-deve...](https://www.udemy.com/machine-learning-intro-for-
python-developers/)

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jackallis
This depends upon you first telling us at what level you are? beg, int, ad.

~~~
cromiium
Not OP but I'm a beginner. Any recommendations different than what was already
said?

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source99
fast.ai is great.

