I went through this recently. I don't think it's hard to get started. Pandas is a great library for getting your data into the necessary format. And the from th

I went through this recently. I don't think it's hard to get started. Pandas is a great library for getting your data into the necessary format. And the from there you can pretty much just pick different machine learning models off the shelf from the scikit-learn library and see what works. For most problems I've applied it to are classification tasks. The part of the code where you actually run the machine learning model is usually only like 2 lines, and you can swap-in different models easily. Two terms that come up when you're testing the model: recall and precision. I found these terms a bit unintuitive. Basically 'recall' is how many of the real matches did you manage to capture (accurately classify/predict), and 'precision' is how wasteful your model was (how many false alarms). Depending on what you're doing one of those things may be much more important than the other. I can actually imagine a world where running a machine learning model is a kind of mundane office task that most people can do, like creating a pivot table.