Julia 1.0 Programming Complete Reference Guide

Learn dynamic programming with Julia to build apps for data analysis visualization machine learning and the webKey FeaturesLeverage Julias high speed and efficiency to build fast efficient applicationsPerform supervised and unsupervised machine learning and time series analysisTackle problems concurrently and in a distributed environmentBook DescriptionJulia offers the high productivity and ease of use of Python and R with the lightning-fast speed of C++. There’s never been a better time to learn this language thanks to its large-scale adoption across a wide range of domains including fintech biotech and artificial intelligence (AI).You will begin by learning how to set up a running Julia platform before exploring its various built-in types. This Learning Path walks you through two important collection types: arrays and matrices. You’ll be taken through how type conversions and promotions work and in further chapters youll study how Julia interacts with operating systems and other languages. You’ll also learn about the use of macros what makes Julia suitable for numerical and scientific computing and how to run external programs.Once you have grasped the basics this Learning Path goes on to how to analyze the Iris dataset using DataFrames. While building a web scraper and a web app you’ll explore the use of functions methods and multiple dispatches. In the final chapters youll delve into machine learning where youll build a book recommender system.By the end of this Learning Path you’ll be well versed with Julia and have the skills you need to leverage its high speed and efficiency for your applications.This Learning Path includes content from the following Packt products:Julia 1.0 Programming - Second Edition by Ivo BalbaertJulia Programming Projects by Adrian SalceanuWhat you will learnCreate your own types to extend the built-in type systemVisualize your data in Julia with plotting packagesExplore the use of built-in macros for testing and debuggingIntegrate Julia with other languages such as C Python and MATLABAnalyze and manipulate datasets using Julia and DataFramesDevelop and run a web app using Julia and the HTTP packageBuild a recommendation system using supervised machine learningWho this book is forIf you are a statistician or data scientist who wants a quick course in the Julia programming language while building big data applications this Learning Path is for you. Basic knowledge of mathematics and programming is a must.