How many times, trying to approach Machine Learning, you felt baffled, disoriented and without a real "path" to follow, to ensure yourself a deep knowledge and the ability to apply it?

This field is crazily exciting, but being rapid and "new" at the same time, it can be confusing to understand what each things means, and have a coherent naming of the things across resources and tutorials.

I recently landed my first internship for a Data Science position in a shiny ML startup. My boss asked me if it was possible to create a study path for me and newcomers, and i've put a lot of efforts to share my 4-5 years of walking around the internet and collecting sources, projects, awesome tools, tutorial, links, best practices in the ML field, and organizing them in a awesome and useable way.

You will get your hands dirty and learn in parallel theory and practice (which is the only efffective way to learn).

The frameworks i've chosen is Scikit-Learn for generic ML tasks and TensorFlow for Deep Learning, and I'll update the document weekly.

No prior knowledge is required, just time and will.

Feel free to improve it and share with everyone.

https://github.com/clone95/Machine-Learning-Study-Path/blob/master/README.md

Inb4: sorry for my english, it's not my native language :)