A great way to level up is by reading the scikit-learn user guide. It’s full of examples, tips, and detailed explanations of methods, including lesser-known ones like Gaussian processes and kernel ridge regression. Taking notes, watching videos, and learning more about these methods as you go will make the process even better.
Starting with Chapter 6 might be helpful, but having some prior knowledge equivalent to an introductory ML course is recommended. After finishing the scikit-learn guide, you could also explore the statsmodels guide, especially the time series analysis (TSA) API, though it’s a bit more challenging.
While reading, try to implement the examples in a project folder. Set up a virtual environment and experiment with the methods. Change parameters and see how the results differ. Hands-on practice is where the real learning happens.
The scikit-learn guide is one of the best free resources for an intermediate-level overview of ML techniques. It covers a lot of ground in an accessible way, something many bigger organizations struggle to do.
To move from intermediate to advanced, try reading the source code for the scikit-learn methods. It’s a deep dive but invaluable for understanding the complexity of ML implementations.
Ridge said:
How long does it typically take to go through the scikit-learn guide?
It depends on your background. If you’re familiar with most methods, it might take a few days. For beginners, it could take weeks, especially if you’re exploring the math in detail.