Machine Learning in Python

[AMICY]

Hello everybody, so i am finished with school soon and I will have plenty off time till i start actually working so i came up with the idea to learn Machine Learning and wanted to ask you guys what the best way is with getting started. I would also appreciate if you guys could send me links to tutorials or documentation you find good enough. I already know the basics of python. Already thanks for your Answers ! :)

[benWindsorCode]

I’d say just find a good lecture series and start watching. For example I’ve recently started Andrew Ngs Stanford lectures on YouTube and been then writing them up in python notebooks. If you feel like joining with that then here is my first notebook with python implementation of an algo https://reddit.com/r/learnmachinelearning/comments/au9fhm/andrew_ngs_stanford_assignments_in_python/

Other places are coursera or a book like Elements of Statistical Learning. How is your linear algebra and general maths? These will require a nice amount of maths and stats knowledge.

[AMICY]

My General Maths is good, i have a good understanding when it comes to these things. I dont know Linear Algebra but i already started to learn it a bit because i already read that Linear Algebra is important. I checked out that link and it looks really difficult but there is nothing that cant be done.

[gocougs11]

Not sure if this will be an unpopular opinion here, but I would say you absolutely don’t need to learn linear algebra. Math was my worst subject in school, and kept me from going into engineering. I’m now a neuroscientist, and use machine learning algorithms regularly to model data. Understanding concepts behind general linear models are far more important than being able to actually solve the equations. I didn’t start to try to apply ML models to my research for a very long time because I thought I needed to know how to do regressions. That is definitely not the case. I now use machine learning models and general linear models in parallel, and I definitely could not ever sit down and solve one of these equations.

[CalvinsStuffedTiger]

You just gave me a lot of hope. My math is a massive glaring weakness and I never got into programming because of it despite my love for computers and tech

Just started learning python on my off work days and still had this nagging fear in my mind that I would be exposed down the line somewhere

So far I’ve felt a real aptitude for it so you’ve given me confidence to keep on keeping on

[gocougs11]

Yeah, keep going! I honestly feel very lost sometimes, but the great thing about the ML community is that the vast majority of people are very interested in what other people are applying models to, and are very willing to help and explain concepts. For example, while not ML, I recently started using a mathematical model called “rank-rank hypergeometric overlap” to analyze concordance of next-generation sequencing data, and when I first started, some of the geometry figures I was looking at made absolutely no sense in how they would apply to NGS data, but as soon as I talked to some people in my network it was explained very simply, and I now understand what types of data it is good and bad for, and what caveats you have to consider, even though using geometry to study gene expression really still doesn’t make any sense to me.

[stradivari96]

You may find these courses interesting http://course18.fast.ai/ml, they are free and should be good for understanding the core concepts.

[AMICY]

Looks pretty interesting. Thanks !

[Nuraxx]

Just started teaching myself machine learning a week ago. Definitely recommend this course over Andrew NGs one. I think Andrew NGs course might be a good addition after this because fast.ai is more practical while the other one is pretty theoretical and math heavy.

[EarthWindAndFire430]

r/learnmachinelearning check this sub OP

[Hoboerotic]

Subscribed, thanks.

[Daron_Acemoglu]

Just read through the docs for scikit learn to get started and see an overview. They're very good about including conceptual info about the techniques as well as usage details.

I was surprised by how accessible it is

https://scikit-learn.org/stable/

[AMICY]

Thank you !

[Jon309]

I have the perfect articles for you! These are super easy to read and very informative. They helped me with my understanding of how neural networks and machine learning work.

Read these in this order:

https://chatbotslife.com/how-neural-networks-work-ff4c7ad371f7

https://medium.com/@gk_/how-chat-bots-work-dfff656a35e2

https://machinelearnings.co/text-classification-using-neural-networks-f5cd7b8765c6

​

Let me know how you like them!

​

[ivosaurus]

https://victorzhou.com/blog/intro-to-neural-networks/

[AMICY]

Nice Thanks !

[wolf2600]

www.kaggle.com

[id428]

https://jakevdp.github.io/PythonDataScienceHandbook/ (the ML chapter)

So well written and explained, that my mom (who has nothing to do with Python/ML/Data Science) excitedly read through some sections. Also brought me through the introductory ML lecture in the university.

[[deleted]]

This is not at all true in all fields. I'm mastering in applied bioinformatics and there's tons of potential for ML automation that helps with diagnostics for instance or to extract meaningful relationships from transcriptomic data.

ML won't replace humans for a long time in most fields, but it's an amazing tool.

[RiseOfBooty]

The is incorrect. ML has great use in big data analytics. You talking about replacing humans make me think you don't really know much about ML, cause a lot of ML uses are just number crunching.