Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

Building machine learning models with ScikitLearn.jl

In this recipe, we show how to use Julia to create machine learning models. We will illustrate this with theScikitLearn.jlpackage. Although our recipe focuses on how to create machine learning models in Julia, we do not focus on the business applications of machine learning. This recipe was inspired by the book Python Machine Learning - Second Edition by S. Raschka and V. Mirjalili.

Note that the ScikitLearn.jl package is not present in the package table of the Preface which is at the beginning of this book nor in the package installation scripts. We took this approach because on one hand ScikitLearn.jl is being developed continuously to map Python code to its Julia equivalent and on the other hand since the goal of this package is to mirror the Anaconda scikit-learn API we expect the ScikitLearn.jl API to be stable over time. Finally, at the moment, ScikitLearn.jl depends on the Anaconda scikit-learn package to be present in the Python...