Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Unsupervised machine learning with clustering


Julia's package ecosystem provides a dedicated library for clustering. Unsurprisingly, it's called Clustering.We can simply executepkg> add Clusteringto install it. TheClusteringpackage implements a few common clustering algorithms—k-means,affinity propagation,DBSCAN,andkmedoids.

The k-means algorithm

The k-means algorithm is one of the most popular ones, providing a balanced combination of good results and good performance in a wide range of applications. However, one complication is that we're required to give it the number of clusters beforehand. More exactly, this number, called k (hence the first letter of the name of the algorithm), represents the number of centroids. A centroid is a point that is representative of each cluster.

The k-means algorithm applies an iterative approach—it places the centroids using the algorithm defined by the seeding procedure, then it assigns each point to its corresponding centroid, the mean to which is closest...