TF-IDF stands for Term Frequency -Inverse Document Frequency, and it is used to determine how important a word is a document in a corpus (a collection of documents). Specifically, the TD-IDF value for a given word increases relative to the number of times a word appears in the document and decreases by the number of documents in the corpus that also contain that particular word. This is to account for words that are used more commonly in general. TF-IDF is a popular technique in the field of Natural Language Processing (NLP) and information retrieval. The Apriori Algorithm is an association rule algorithm and is most commonly used to determine groups of items that are most closely associated with each other in an itemset.

Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.