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

Machine learning-based recommendations

Julia's ecosystem provides access to Recommendation.jl, a package that implements a multitude of algorithms for both personalized and non-personalized recommendations. For model-based recommenders, it has support for SVD, MF, and content-based recommendations using TF-IDF scoring algorithms.

There's also another very good alternative—the ScikitLearn.jl package ( This implements Python's very popular scikit-learn interface and algorithms in Julia, supporting both models from the Julia ecosystem and those of the scikit-learn library (via PyCall.jl). The Scikit website and documentation can be found at It is very powerful and definitely worth keeping in mind, especially for building highly efficient recommenders for production usage. For learning purposes, we'll stick to Recommendation, as it provides for a simpler implementation.

Making recommendations with Recommendation

For our...