This article was originally published at https://programmerbackpack.com/.
Machine Learning has been a buzzword for quite a few years right now and it's the go-to thought for developers wanting to build something cool. One of the things that scare me as a developer about this subject is that it's a lot to take. New research appears every day, new projects over the night and it's really really really hard to keep track of everything.
Another thing equally important is that Machine Learning requires knowledge about Maths and Statistics and so many other fields that I may never feel confident enough to say "One day I will become a Machine Learning expert" 😀
But still, with a lot of research going on in this field, one good thing is that there is a huge list of Machine Learning resources available online(and a lot of them are free) and that includes papers, blog posts and, most important of all, open-source projects.
I love open-source Machine Learning projects(OS projects in general rock!) because they allow me, a beginner to Machine Learning, to play with different concepts in order to understand more and maybe contribute in my own way.
Thank you so much for reading this. Interested in more stories like this? Follow me on Twitter at @b_dmarius and I'll post there every new article.
Having all of these said, let's go to see the top 10 Machine Learning repositories on Github.
Tensorflow (143k stars)
Tensorflow is an open-source Machine Learning framework and it's the go-to framework for many Machine Learning projects. It can be used in Python and C++ but other, unofficial APIs are provided for other programming languages. It is used by lots of big companies and also other open-source projects are built on top of Tensorflow.
Awesome Machine Learning (44.1k stars)
Awesome Machine Learning is a curated collection of Machine Learning projects, software and other resources that a software developer can use during his/her journey of becoming a Machine Learning engineer. The collection of frameworks is organised by programming language and in general the resources are grouped by a sub-field(Computer Vision, NLP and so on).
Scikit-Learn (40k stars)
If you've read at least 3 tutorials about Machine Learning, chances are that in at least 2 of them you've seen this library being used. This is the go-to framework for many Machine Learning projects because it is very powerful, relatively easy to use and very fast.
Machine Learning for software engineers (23.6K stars)
This is another collection of resources for becoming a Machine Learning engineer, but this time more focused on learning resources than on software. It contains references to courses, videos, interviews and preparing for Machine Learning interviews.
Hands-on ML (19.3k stars)
A collection of Jupyter Notebooks that contains code and solutions to exercises for the "Hands-on Machine Learning with Scikit-Learn and TensorFlow" book written by the author of this repository. It contains lots of examples and code implementations for different subjects explained in the book and it's easy to use since it is a collection of Jupyter Notebooks.
Machine Learning from Scratch (15.8k stars)
This repository contains implementations in NumPy and for different Machine Learning models and algorithms, ranging from easy subjects as linear regression to more advanced topics on deep learning. The code is contained in Jupyter Notebooks which are, again, easy to test.
Cheatsheets AI (13.2k stars)
A collection of cheatsheets about different subjects addressed to machine learning and deep learning engineers. This is very useful if you want to refresh your memory on some Machine Learn knowledge or you could use it as a reference that can guide you on your Machine Learning path.
Machine Learning projects come and go every day, but the most important of them stick and will be here to help us all in our journey of learning and applying Machine Learning to our projects.
This list changes frequently as more and more repositories appear everyday. You can always search for new projects by using the Github search page.
Thank you so much for reading this. Interested in more stories like this? Follow me on Twitter at @b_dmarius and I'll post there every new article.
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