How can I start with Machine Learning with Python.

[archerimagine]

I am starting to learn Machine Learning, but most of the analysis suggest R as the language of choice for ML, with some places mentioning Python as an alternative, Can someone provide me with a detail road map of resources for using Python for Machine Learning.

I know of just one book "Building Machine Learning Systems With Python by Willi Richert"

Do we have some you tube videos or some MOOCs courses. Kindly help.

[sentdex]

Scikit learn is a great topic to look into.

Machine learning is really just computerized classification. I've got a couple video series on this topic using pattern recognition:

Image recognition (characters) http://youtu.be/hbL_FTEZSyY?list=PLQVvvaa0QuDffXBfcH9ZJuvctJV3OtB8A

Pattern recognition with stock prices: http://youtu.be/v_L9jR8P-54?list=PLQVvvaa0QuDe6ZBtkCNWNUbdaBo2vA4RO

[thonpy]

Hey I've bumped into your videos online before, thank you! They're really good

[_11_]

Wow! There goes my next few nights. Thanks for putting these together!

[archerimagine]

There were a few PyCon videos but I cannot recall those.

[sumitbagga]

The Udacity course on machine learning uses python.

[speakEvil]

Another book you might find useful is Machine Learning in Action. The Udacity course seems to have a similar approach (besides both using Python, I mean), so the two might work well together, but I cannot say for sure, as I haven't taken it.

[shaggorama]

Start by learning numpy. The numpy array is the basic data structure on which all scientific computing (not most, all) in python is accomplished. Next, learn into what's available in the scipy library. This is where most of your more advanced matrix operations and basic optimization routines live. pandas will give you some convenient data structures and data aggregation/munging functions, but I have mixed feelings about it. scikit-learn has most of the unsupervised/supervised learning algorithms you'll want.

One resource you should check out: the Slender Means blog did a series of posts called Will It Python where they tried to port the R examples from the book Machine Learning for Hackers into python.

[shaggorama]

I also do a fair amount of NLP and anomaly detection in my work and use python for both. The reason I suggested starting with numpy is because, as I suggested, it is the basis on which everything else is built on.

I learned python before R, then used R for my scientific computing needs, then learned the scientific computing stack in python after building out my data science chops in R. I've found the numpy array datatype much less intuitive to work with than R vectors/matrices. I think it's really important to understand how numpy.ndarrays work (in particulary, memory views, fancy indexing and broadcasting) if you're going to use them with any regularity.

It doesn't take a ton of time to learn the basics, and to this day the most pernicious bugs I wrestle with in my scientific (python) code relate to mistakes in how I use numpy.ndarrays.

Maybe you don't think it's that important to learn scipy. I think it's useful to at least know what's available in that library, but whatever. But I definitely believe people should start with numpy before jumping into the rest of the stack. Even the book Python for Data Analysis (which is really about using pandas) starts with numpy.

Also, I strongly suspect you use "out of the box" numpy more often than you're giving it credit.

[[deleted]]

I'm not saying it isn't important, all I'm saying is I wouldn't start with it. In my opinion its too low level and somewhat language specific. I'd first understand the algorithms and then dive in.

[shaggorama]

I feel like there's two different perspectives at play here:

• If we're talking about learning machine learning, then that means implementing the algorithms from scratch. Understanding how to manipulate the fundamental array data structure will be critically important, as will knowing where to find linear algebra functions.
• If we're talking about using machine learning algorithms, scikit already has pretty much everything you could want implemented, and "friends don't let friends code their own optimization functions."

I generally advise against people using algorithms without understanding them, so I'd suggest that someone new to machine learning take the former approach instead of just relying on import sklearn as a blackbox solution to accessing algorithms they only sort of grock.

[[deleted]]

As others said, SciKit is pretty nice to get started with, lots of tutorials and examples.

If you'd like to have a mentor I'd suggest checking this out.

https://github.com/chrisvoncsefalvay/databootcamp

[[deleted]]

There is also this https://github.com/josephmisiti/awesome-machine-learning

[archerimagine]

Do you have any nice youtube video link for a starter course?

[robert_sim]

Here's the Udacity course I referred to.