▶What You Will Learn
- Create your own types to extend the built-in type system
- Visualize your data in Julia with plotting packages
- Explore the use of built-in macros for testing and debugging
- Integrate Julia with other languages such as C, Python, and MATLAB
- Analyze and manipulate datasets using Julia and DataFrames
- Develop and run a web app using Julia and the HTTP package
- Build a recommendation system using supervised machine learning
▶Key Features
- Leverage Julia's high speed and efficiency to build fast, efficient applications
- Perform supervised and unsupervised machine learning and time series analysis
- Tackle problems concurrently and in a distributed environment
▶Who This Book Is For
If 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.
▶What this book covers
- Chapter 1, Installing the Julia Platform, explains how to install all the necessary components for a Julia environment. It teaches you how to work with Julia's console (the REPL) and discusses some of the more elaborate development editors you can use.
- Chapter 2, Variables, Types, and Operations, discusses the elementary built-in types in Julia and the operations that can be performed on them so that you are prepared to start writing code with them.
- Chapter 3, Functions, teaches you why functions are the basic building blocks of Julia, and how to effectively use them.
- Chapter 4, Control Flow, shows Julia's elegant control constructs, how to perform error handling, and how to use coroutines (called Tasks in Julia) to structure the execution of your code.
- Chapter 5, Collection Types, explores the different types that group individual values, such as arrays and matrices, tuples, dictionaries, and sets.
- Chapter 6, More on Types, Methods, and Modules, digs deeper into the type concept and how it is used in multiple dispatch to get C-like performance. Modules, a higher code organizing concept, are discussed as well.
- Chapter 7, Metaprogramming in Julia, touches on deeper layers of Julia, such as expressions and reflection capabilities, and demonstrates the power of macros.
- Chapter 8, I/O, Networking, and Parallel Computing, shows how to work with data in files and databases by using DataFrames. It also looks at networking capabilities, and how to set up a parallel computing environment with Julia.
- Chapter 9, Running External Programs, looks at how Julia interacts with the command-line and with other languages and also discusses performance tips.
- Chapter 10, The Standard Library and Packages, digs deeper into the standard library, and demonstrates important packages for the visualization of data.
- Chapter 11, Creating Our First Julia App, will show you how to perform data analysis against the Iris dataset with Julia. We take a look at RDatasets, a package that provides access to 700 learning datasets distributed with the R language. We'll load the Iris dataset and we'll manipulate it using standard data analysis functions. We also look more closely at the data by employing common visualization techniques using Gadfly. In the process, we cover strings and regular expressions, numbers, tuples, ranges, and arrays. Finally, we'll see how to persist and (re)load our data with CSV, Feather, and MongoDB.
- Chapter 12, Setting Up the Wiki Game, introduces our first fully featured Julia project, a Wikipedia web crawler disguised as a popular game. In the first iteration, we will build a program that gets a random web page from Wikipedia. Then we'll learn about parsing the HTML response using CSS selectors. We'll use this to introduce key concepts such as functions, pairs, dictionaries, exceptions, and conditional evaluation.
- Chapter 13, Building the Wiki Game Web Crawler, will build upon the foundations set in the previous chapter, and we'll build a Wikipedia web scraper that implements the requirements of the wiki game.
- Chapter 14, Adding a Web UI for the Wiki Game, will see us finish the Wiki Game by adding a web UI. We'll build a simple web app that will allow the player to start a new game, render the Wikipedia articles picked by the game engine, and navigate between linked Wikipedia articles. The UI will also keep track of and display current game progress and determine a session as a win or a loss.
- Chapter 15, Implementing Recommender Systems with Julia, will have you take on a more challenging example project and build a few basic recommender systems. We'll set up a supervised machine learning system powered by Julia and we will develop some simple movie recommenders.
- Chapter 16, Machine Learning for Recommender Systems, will show you how to implement a more powerful recommender system using the Recommender.jl package. We will use a sample dataset to train our system and generate book recommendations as we'll learn about model-based recommenders.
