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Julia Programming Projects

By : Adrian Salceanu

4 (10)

Julia Programming Projects

4 (10)

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

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Free Chapter

Getting Started with Julia Programming

Getting Started with Julia Programming

Technical requirements

Why Julia?

Installing Julia

Choosing an IDE

Getting started with Julia

The package system

Summary

Creating Our First Julia App

Creating Our First Julia App

Technical requirements

Defining variables

Comments

Strings

Regular expressions

Raw string literals

Numbers

Tuples

Ranges

Arrays

Exploratory data analysis with Julia

Using simple statistics to better understand our data

Loading and saving our data

Summary

Setting Up the Wiki Game

Setting Up the Wiki Game

Technical requirements

Data harvesting through web scraping

Understanding HTML documents

Accessing the internet from Julia

Handling HTTP responses

Understanding HTTP responses

Dictionaries

Using the HTTP response

Coding defensively

Handling errors like a pro

Learning about functions

Writing a basic web crawler – take one

Carrying on with the crawler's implementation

Summary

Building the Wiki Game Web Crawler

Building the Wiki Game Web Crawler

Technical requirements

Six Degrees of Wikipedia, the gameplay

Organizing our code

Setting up our game's architecture

Building our Wikipedia crawler - take two

Implementing the gameplay

Finishing touches

Learning about Julia's type system

Using article types

Methods

Working with relational databases

Summary

Adding a Web UI for the Wiki Game

Adding a Web UI for the Wiki Game

Technical requirements

The game plan

Learning about Julia's web stack

Developing the game's web UI

Starting a new game

Displaying a Wikipedia article page

Showing the solution

Handling any other requests

Wrapping it up

Summary

Implementing Recommender Systems with Julia

Implementing Recommender Systems with Julia

Technical requirements

Understanding recommender systems

Classifying recommender systems

Summary

Machine Learning for Recommender Systems

Machine Learning for Recommender Systems

Technical requirements

Comparing the memory-based versus model-based recommenders

Machine learning-based recommendations

Testing the recommendations

Learning about hybrid recommender systems

Summary

Leveraging Unsupervised Learning Techniques

Leveraging Unsupervised Learning Techniques

Technical requirements

Unsupervised machine learning

Clustering

Data analysis of the San Francisco business

Unsupervised machine learning with clustering

Summary

Working with Dates, Times, and Time Series

Working with Dates, Times, and Time Series

Technical requirements

Working with dates and times

Constructing dates and times

Working with date and time accessors

Defining the date ranges

Period types and period arithmetic

Date adjustments

Rounding of dates

Adding support for time zones

Time series data in Julia

Summary

Time Series Forecasting

Time Series Forecasting

Technical requirements

A quick look at our data

Understanding time series components

Time series decomposition

Time series stationarity

Time series forecasting

Summary

Creating Julia Packages

Creating Julia Packages

Technical requirements

Creating a new Julia package

Defining the requirements for our package

Beginning with test-driven Julia development

Performance testing

Interacting with the GitHub API

Documenting our package

Advanced documentation tips

Registering our package

Summary

Other Books You May Enjoy

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Understanding HTML documents

In order to extract data from the fetched web pages, we need to isolate and manipulate the structural elements that contain the desired information. That's why a basic understanding of the generic structure of the web pages is helpful when performing web scraping. If you've done web scraping before, maybe using a different programming language, or if you just know enough about HTML documents, feel free to skip this section. On the other hand, if you're new to this or just need a quick refresher, please read on.

Hypertext Markup Language (HTML) is the gold standard for creating web pages and web applications. HTML goes hand in hand with HTTP, the protocol for transmitting HTML documents over the internet.

The building blocks of HTML pages are the HTML elements. They provide both the content and the structure of a web page. They can...

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