Explore Julia, the next-generation language, for advancing in the field of data science, machine learning, and numerical computing
About This Video
- Learn the syntax of Julia and its differences from Python
- Learn machine learning models, both traditional and deep
- Explore data science case studies, including analysis and clustering
The objective of this course is to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.
We won't waste time with the theory of why Julia is fast. We will jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.
After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.
This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!
By the end of the course, you will have a strong understanding of Julia programming language fundamentals.
Who this book is for
This course is for all levels of data science and machine learning practitioners aiming to enhance their abilities and skill level in DS and ML. Developers who want to know how to harness the power of big data can also go for this course.
A basic understanding of programming is a must. Understanding Python, basic data science (reading CSVs and so on), and basic concepts of deep learning (such as classification) is not necessary but would be helpful.
Table of contents
- Chapter 1 : Introduction and Setting Up
- Chapter 2 : Core Language Basics
- Chapter 3 : Arrays and Matrices: Native Language Support
- Chapter 4 : Functions and Fun Stuff
- Chapter 5 : Getting Started with Data Science
- Chapter 6 : Case Studies in Data Science
- Chapter 7 : Deep Learning - Flux in Julia
- Chapter 8 : Parting Words
- Title: Julia Programming Language - From Zero to Expert
- Release date: September 2021
- Publisher(s): Packt Publishing
- ISBN: 9781803230719
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