Julia Programming Projects: Learn Julia 1.x by building apps for data analysis, visualization, machine learning, and the web by Adrian Salceanu
A step-by-step guide that demonstrates how to build simple-to-advanced applications through examples in Julia Lang 1.x using modern tools
Key Features- Work with powerful open-source libraries for data wrangling, analysis, and visualization
- Develop full-featured, full-stack web applications
- Learn to perform supervised and unsupervised machine learning and time series analysis with Julia
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.
What you will learn- Leverage Julia's strengths, its top packages, and main IDE options
- Analyze and manipulate datasets using Julia and DataFrames
- Write complex code while building real-life Julia applications
- Develop and run a web app using Julia and the HTTP package
- Build a recommender system using supervised machine learning
- Perform exploratory data analysis
- Apply unsupervised machine learning algorithms
- Perform time series data analysis, visualization, and forecasting
Data scientists, statisticians, business analysts, and developers who are interested in learning how to use Julia to crunch numbers, analyze data and build apps will find this book useful. A basic knowledge of programming is assumed.