inside-front-cover

inside front cover

 

Core algorithms inside the book

Algorithm

Use case

First introduced

K-means

Clustering

Section 10

DBSCAN

Clustering

Section 10

Jaccard similarity computation

Text comparison

Section 13

Cosine similarity computation

Text comparison

Section 13

Principal component analysis

Dimension reduction

Section 14

Singular value decomposition

Dimension reduction

Section 14

Power iteration

Eigenvector computation

Section 14

TFIDF vectorization

Text comparison

Section 15

Shortest path length computation

Network path optimization

Section 18

PageRank

Network centrality measurement

Section 19

Markov clustering

Social network clustering

Section 19

K-nearest neighbors

Supervised classification

Section 20

Cross-validation

Model performance testing

Section 20

Perceptron

Supervised classification

Section 21

Linear regression

Supervised classification

Section 21

Decision tree

Supervised classification

Section 22

Random forest

Supervised classification

Section 22
A trained logistic regression classifier distinguishes between two classes of points by slicing like a cleaver through 3D space (see section 21).