Julia Programming Certification Training Course Overview

Julia is a programming language developed by researchers of MIT. It has been designed by keeping in mind the future of computing like: high-performance computing, parallel computing and machine learning. Usually with the help of programming a programmer tells the computer what to do, but now a days programming is not only about telling the computer what to do. There are some new areas of science which have recently emerged up, like machine learning, big data and AI. Special kind of programming is required in these fields and Julia is one such programming language. Julia is a perfect platform for the above technologies because it is one of the fastest language as well as fastest growing language today. Fields like machine learning and AI depends a lot on the performance and Julia fits much better in this because Julia code can run as fast as C code and sometimes even faster. In the past few years we have seen an exponential growth in the field of scientific computing which was done by some other good languages, but they may not have been the optimal languages for the task. Julia by contrast has been developed by keeping in mind the performance as prime factor for the above technologies. Some of the features of Julia are just in time compilation, interoperability with python C fortron, open source and cross platform.


Julia Programming (32 Hours) Download Course Contents

Live Virtual Classroom
Group Training 1150
15 - 18 Nov 09:00 AM - 05:00 PM CST
(8 Hours/Day)

1-on-1 Training (GTR) 1350
4 Hours
8 Hours
Week Days

Start Time : At any time

12 AM
12 PM

GTR=Guaranteed to Run
Classroom Training (Available: London, Dubai, India, Sydney, Vancouver)
Duration : On Request
Fee : On Request
On Request
Special Solutions for Corporate Clients! Click here
Hire Our Trainers! Click here

Course Modules

Module 1: Introduction and Setting Up Your Julia Environment
  • Programming and Its Impacts
  • Why Julia?
  • Rationale Behind Learning Julia
  • What Does This Book Aim to Cover?
  • Setting Up to Work with Julia
Module 2: Variables and Input
  • What Are Variables?
  • Using Simple Variables in Julia to Store Information
  • Taking, Storing, and Using Input from a User
  • Operators and Syntax
  • Types of Variables
  • Converting Between Variables
Module 3: Conditions and Iterations
  • What Is a Condition?
  • What Are Conditional Operators?
  • How Computers Make Decisions Using if/elseif/elseStatements
  • What Are Iterations?
  • How to Iterate Using for Loops
  • How to Iterate Using while Loops
Module 4: Arrays and Dictionaries
  • What Are Arrays and Why Are They Required?
  • How to Create, Go Through, and Modify Arrays
  • Some Operations on Arrays
  • What Are Dictionaries and What Are Their Advantages Over Arrays?
  • Creating and Using Dictionaries
  • Building the Lending App Using Dictionaries
  • Some Useful Functions Available in Julia
Module 5: Functions
  • Functions and How to Use Them
  • Functions Help in Reducing Errors and Easy Maintenance of Code
  • Declaring and Calling Functions
  • Functions That Return Values
  • Functions with Optional Keyword Arguments
  • Applying Functions on Arrays
  • Generic Functions
  • Using Functions Recursively
Module 6: Handling Errors and Exceptions
  • Bugs and Debugging
  • What Are Errors?
  • What Are Exceptions?
  • Tricks to Find and Eradicate Bugs
Module 7: Package Management
  • What Are REST APIs?
  • How to Install and Use Packages
  • Multiprocessing and How It’s Used in Julia
  • Calling Code from Other Languages
Module 8: Reading and Writing Files
  • Why Are Files Useful?
  • How Do You Read a File in Julia?
  • How Do You Write to Files in Julia?
  • Creating a Caesar Cipher in Julia
Module 9: How Machines Learn
  • What Is Machine Learning?
  • How Does Machine Learning Work?
  • Style Transfer Using Flux
  • A Primer on the Calculus Behind Machine Learning
  • Using Flux’s Automatic Differentiation to Train a Simple Perceptron
Download Course Contents

Request More Information

Course Prerequisites
  • Basic Understadning of Mathamatics and Programming .

A High Performance Language for Artificial Intelligecne and Machine learning.

  • It's easy to learn as the syntax is easy.
  • Good for scientific computing.
  • Good for multi-processing.
  • No special glue required to connect with other languages like C, fortron n python.