Lazy learning - how to manage with the minimal initial investment

The cultivation of certain abilities doesn’t require a great deal of investment, if any. You can often improvise with the little equipment already at your disposal to train just enough of a certain ability to proceed to the next level. At that point you can determine if further pursuit warrants financial investment. I’m fairly pedantic in making even a minimal investment in an enterprise until fair certainty of progress and saturation of available resources. Beyond that, I find the exercise of creativity in harnessing existing resources a rewarding experience in it’s own right.

I consider the above approach lazy learning, although I overloaded the term in presence of a series of alternate definitions likely in existence.

Optional trivia: In Machine Learning, lazy learners are those just-in-time learning classifiers that experience a low cost to train the model with a new data point, but a higher-cost classification op for a query point. A K-Nearest-Neighbor classifier is one example of a lazy learner. A linear regression classifier is the opposite - an eager learner. It experiences a higher cost to retrain the model with a new data point, but a low cost to classify an element.

What activities lend themselves to lazy learning? I should mention that the following list somewhat overlaps with the respective Pareto principle list since lazy-learning resource simplification shares a common philosophy with minimizing the process to achieve the majority of the desired outcome.