Read Generalized Linear Models (GLM), focusing on section one, Logistic Regression.
- Check out this deck introducing logistic regression.
- Read William King's logistic regression tutorial with examples in
R. It explains terms nicely and gives good illustrative examples.
Optional:
- Read Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. This will likely help you to better understand both Naive Bayes and logistic regression, and how they can be thought of as related.
- The UCLA Institute for Digital Research and Education has a lot of resources on using statistical software, such as: R Data Analysis Examples: Logit Regression.
- For a few general multiclass reduction approaches, read these papers on Weighted One-Against All and Error-Correcting Tournaments.
- Read much more on GLMs with a chapter on the topic.
- Look through a notebook on logistic regression with statsmodels for well switching in Bangledesh.