Master Julia Programming Online Course

Master Julia Programming Online Course

This course is designed to quickly build a solid foundation in Julia, focusing on hands-on coding rather than lengthy theory. You’ll start with the core concepts of the language and then move into practical applications in data science, machine learning, and deep learning. Through case studies and projects, you’ll learn how to build models from scratch and work with advanced techniques efficiently. By the end, you’ll have a strong grasp of Julia fundamentals and the confidence to apply them in real-world scenarios.

Who should take this course?

This course is ideal for developers, data engineers, system administrators, and anyone who works with JSON data regularly. Beginners in command-line tools who want to learn efficient ways to parse, filter, and manipulate JSON will also benefit greatly.

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

Course Outline

Introduction and Setting Up

• Introduction
• Installation
• Packages and Interactive Notebook

Core Language Basics

• Basic Syntax, Variables and Operations
• Control Structures, Iterations, and Ranges
• Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples
• Dictionaries (Maps) and Symbols in Julia

Arrays and Matrices: Native Language Support

• Arrays, Matrices, Tensors, Reshaping, Helper Functions
• Data Type Details, Casting Among Types

Functions and Fun Stuff

• Defining Functions, Overloading, Multiple-Dispatch
• Anonymous Functions (and their importance), Splatting and Slurping
• Functional Programming, Broadcasting - Most Important Concept in Julia
• Interfacing with Python and R

Getting Started with Data Science

• Plotting Basics - Prettier Julia Plots
• Data Wrangling, Reading CSV Files, Descriptive Case Study
• Further Data Manipulation, Apache Arrow, Grouping, and Analysis

Case Studies in Data Science

• Case Study: Clustering for Housing/Map Data
• Classification with Decision Trees/Random Forests

Deep Learning - Flux in Julia

• Writing a Neural Network from Scratch in a Few Lines
• Multiple Layers, State-of-the-Art in a Few More Lines
• Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls
• MNIST Continued, Creating the Deep Model, Training and Testing
• Saving and Loading Models, Exploring More Options

Parting Words

• Where to Go from Here: Pointers for Further Learning