So you have a research project where you use standard machine learning approaches to analyse text. A grant brought you money for a research assistant. But: students in the department have to overcome a bit of a knowledge gap if you want to employ them as RAs on your project.
While nowadays students in social sciences often do have a basic training in statistical research methods, they are often not at a level where they can help doing data science with text.
I put together a couple of sources that I found useful to bring some students up to speed. The workout is a mixed bag of a) learning how to handle text b) how to use Python and c) how to analyse text data using machine learning.
There is no particular order in learning these elements. The best way to approach it is to work on a problem from the project — and then figure out the knowledge and skills that are still lacking.
Learning Machine Learning for Text in Python:
Scikit Learn Tutorial: https://scikit-learn.org/stable/tutorial/index.html
A Nice Intro Class from PolSci by Pablo Barberá & Ken Benoit at LSE: https://lse-my459.github.io/#week-5-machine-learning-for-texts
Learning Machine Learning
Applied Predictive Modeling (R), Kuhn, Max and Johnson, Kjell, https://www.springer.com/gb/book/9781461468486
Online Course: https:/www.coursera.org/learn/machine-learning
Running Python Notebooks Online: https://colab.research.google.com/notebooks/welcome.ipynb
Online Class With Fundamentals: https://www.codecademy.com/learn/learn-python-3