Explore two important conceptual definitions for supervised learning – features and labels in a dataset.
Here's what I've written (so far):
Explore the concepts of pass by value and pass by reference.
Explore the problem of finding the diameter of a graph.
Explore the main conceptual and technical differences between Big Data and Data Mining.
Explore various write policies used in caches.
A quick and practical guide on when to apply data normalization.
Explore the information-theoretic explanation of the difference between labeled and unlabeled data.
Study the theoretical foundations of the problem of emotion detection in texts.
A comprehensive overview of Naive Bayes Classification.
A comprehensive introduction to Entropy.