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


Recommender systems represent a very active and dynamic field of study. They started initially as a marginal application of machine learning algorithms and techniques, but due to their practical business value, they have become mainstream in recent years. These days, almost all major programming languages provide powerful recommendations systems libraries—and all major online businesses employ recommenders in one form or another.

Julia is a great language for building recommenders due to its excellent performance. Despite the fact that the language is still young, we already have a couple of interesting packages to choose from.

Now, you have a solid understanding of the model-based recommendation systems and of their implementation workflow—both on a theoretical and practical level. Plus, throughout our journey, we've also been exposed to more advanced data wrangling using DataFrames, an invaluable tool in Julia's data science arsenal.

In the next chapter, we'll further improve our...