These hands-on projects will level-up your Julia skills for Data Science, Machine Learning, and more.
In Julia for Data Science youβll take on challenging real-world projects that teach you core skills like
Ingestion, analysis, and manipulation of data
Producing stunning data visualizations
Creating supervised and unsupervised learning algorithms
Developing deep learning algorithms
Deploying machine learning algorithms
Packaging code
Building web apps
Julia for Data Science tests and improves your Julia skills on the kind of tasks data scientists perform every day. These challenging projects will work out your Julia skills for importing, cleaning, manipulating, and visualizing data. As you read, youβll learn to take advantage of Juliaβs full potential, as you develop high-performance Deep Learning algorithms and tackle supervised and unsupervised learning.
about the technology
When you think βlanguage flexibility,β think Julia. Designed to solve the βtwo-language problemβ, Julia offers the best of both worlds: the simple and flexible syntax you need for data exploration, and the lightning-fast execution speeds demanded for production deployment. Plus, its growing ecosystem of data science libraries and ability to convert code to run on GPUs make Julia a real contender for building complex Machine Learning applications that donβt leave you waiting days to see results.
about the book
Julia for Data Science challenges you with real-world projects like reading song lyrics from multiple text files and converting them into a data table, preparing credit application data for model development, and more. Youβll dive into developing powerful deep learning algorithms, and learn how Julia streamlines machine learning deployment. Youβll even pick up some new general purpose programming skills that are incredibly useful as a data scientist, including creating packages, building web apps, and writing domain-specific languages.
Starting with the first three chapters, you will be introduced to the core principles of Julia programming, beginning with the fundamentals and gradually moving to more advanced topics. As you gain confidence in Julia, the book will explore supervised learning, unsupervised learning, and deep learning algorithms using Julia. Weβll not only utilize popular Julia packages for these purposes but also teach you how to develop these models from scratch whenever possible.
about the reader
For data scientists who know the absolute basics of Julia and want to upgrade their skills.
about the author
Ilker Arslan, Ph.D., is currently a Chief Information Officer at a finance firm. He has more than 20 years of experience in data science and analytics, has authored two books on data science and statistical computing, and has published various papers on economics.