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
About Packt


In this chapter, we looked into unsupervised machine learning techniques with Julia. We focused on clustering, one of the most widely used applications of unsupervised learning. Starting with a dataset about businesses registered in San Francisco, we performed complex—but not complicated, thanks to Query—data cleansing. In the process, we also learned about metaprogramming, a very powerful coding technique and one of Julia's most powerful and defining features.

Once our data was in top shape and after mastering the basics of clustering theory, using the k-means algorithm, we got down to business. We performed clustering to identify the areas with the highest density of companies to help our imaginary customer, ACME Recruiting, to target the best areas for advertising. After identifying the parts of the city that would give ACME the best reach, we performed data analysis to get the top domains of activity required by our customer so they could build a database of relevant candidates...