Julia Programming Language - From Zero to Expert

Video description

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.

What You Will Learn

  • Learn coding in Julia programming language
  • Use DataFrames (equivalent to Pandas) in Julia
  • Create ML models from scratch in a way that helps you make modifications easily
  • Learn data wrangling with Julia
  • Use Julia to perform data manipulation, Apache Arrow, grouping, and analysis
  • Classify using decision trees and random forests


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.

About The Author

Dr. Mohammad Nauman: Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.

He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!

Publisher resources

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Table of contents

  1. Chapter 1 : Introduction and Setting Up
    1. Introduction
    2. Installation
    3. Packages and Interactive Notebook
  2. Chapter 2 : Core Language Basics
    1. Basic Syntax, Variables and Operations
    2. Control Structures, Iterations, and Ranges
    3. Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples
    4. Dictionaries (Maps) and Symbols in Julia
  3. Chapter 3 : Arrays and Matrices: Native Language Support
    1. Arrays, Matrices, Tensors, Reshaping, Helper Functions
    2. Data Type Details, Casting Among Types
  4. Chapter 4 : Functions and Fun Stuff
    1. Defining Functions, Overloading, Multiple-Dispatch
    2. Anonymous Functions (and their importance), Splatting and Slurping
    3. Functional Programming, Broadcasting - Most Important Concept in Julia
    4. Interfacing with Python and R
  5. Chapter 5 : Getting Started with Data Science
    1. Plotting Basics - Prettier Julia Plots
    2. Data Wrangling, Reading CSV Files, Descriptive Case Study
    3. Further Data Manipulation, Apache Arrow, Grouping, and Analysis
  6. Chapter 6 : Case Studies in Data Science
    1. Case Study: Clustering for Housing/Map Data
    2. Classification with Decision Trees/Random Forests
  7. Chapter 7 : Deep Learning - Flux in Julia
    1. Writing a Neural Network from Scratch in a Few Lines
    2. Multiple Layers, State-of-the-Art in a Few More Lines
    3. Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls
    4. MNIST Continued, Creating the Deep Model, Training and Testing
    5. Saving and Loading Models, Exploring More Options
  8. Chapter 8 : Parting Words
    1. Where to Go from Here: Pointers for Further Learning

Product information

  • Title: Julia Programming Language - From Zero to Expert
  • Author(s): Dr. Mohammad Nauman
  • Release date: September 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781803230719