Julia Programming Language - From Zero to Expert

Name: Julia Programming Language - From Zero to Expert
Rating: 4.4 (282 reviews)

Learn the Next Gen Language for Data Science, Machine Learning and Numerical Computing

Created byDr. Mohammad Nauman

Last updated 3/2022

English

What you'll learn

Syntax of Julia (and differences from Python)
Stength of Julia in terms of data science and machine learning
DataFrames (equiv. to Pandas) in Julia
Data science case studies including analysis and clustering
Machine learning models both traditional and deep
Create ML models from scratch in a way that helps you make modifications easily
Generative Adversarial Networks (GANs)

Course content

9 sections • 34 lectures • 4h 0m total length

Installing Julia (Windows, Linux and MacOS)3:14
Install the recommended environment for Julia development and interactive experiments.
Packages and Interactive Notebook4:56
Setup a Jupyter notebook environment for Julia for quick experimentation.
End of Section Tasks0:09

Basic Syntax, Variables and Operations11:15
See how Julia is different from other languages you might have already worked with.
Control Structures, Iterations and Ranges4:23
Learn the core structure of the Julia language focusing on its novelties as well as strengths.
Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples9:49
See more complex data structures available in Julia.
Dictionaries (Maps), Symbols in Julia3:47
Continue with the most often used structures in Julia.
End of Section Tasks0:26

Defining Functions, Overloading, Multiple-Dispatch12:19
Learn basic function definitions and the differences between methods and functions.
Anonymous Functions (and their importance), Splatting and Slurping9:43
See some novel features of Julia when it comes to function definitions.
Functional Programming, Broadcasting - Most Important Concept in Julia11:05
Learn one of the most elegant ways Julia makes data science and ML code beautiful - broadcasting!
Interfacing with Python and R4:09
See how you can call your existing Python or R code from within Julia with just a couple of lines of code.
End of Section Tasks0:25

Writing a Neural Network from Scratch in a Few Lines16:04
See the awesome Flux library and how it handles most of the grunt work in deep learning automatically for you.
Multiple Layers, State-of-the-Art in a Few More Lines7:35
Scale up your ML model to several layers easily with absolutely elegant code.
Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls14:14
Real-world case study with in-depth analysis of code.
MNIST Continued, Creating the Deep Model, Training and Testing9:02
Continue with the case study from previous lecture.
Saving and Loading Models, Exploring More Options5:38
Learn how to handle training of models that take a long time and must be reused later on.
End of Section Tasks0:20

Basics of GANs - Motivation and Architecture8:50
Study the background, rationale, and core idea behind Generative Adversarial Networks (GANs).
Google Colab and the GPU0:27
Using Google Colaboratory for GPU Usage in Julia6:31
Learn how to get the free GPU on Google Colab working Julia. Set it up in just a couple of minutes and save/load data from Google Drive for persistent storage.
GAN Code, Setup MNIST, Execution and Results12:44
Code up GANs and generate never-before-seen digits similar to MNIST.
Bonus Lecture0:31

Requirements

Basic understanding of programming
Python would be helpful but not necessary
Understanding of basic Data Science (reading CSVs etc) would be helpful
Understanding of basic concepts of Deep Learning (such as classification) would be useful

Description

UPDATE: Added section on Generative Adversarial Networks (GANs)

In the fast-paced world of Data Science and Machine Learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for Data Science and Machine Learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!

That is the objective of this course: 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.

In this course, we take a code-oriented approach. We don't waste time with the theory of why Julia is fast. We 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.

We take a case-study-based approach. 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! Take a look at the promo for this course (and contents list below) for the topics you will learn as well as the preview lectures to get an idea of the interactive style of learning.

Remember: The reason you pay for this course is support. I reply within the day. See any of my course reviews for proof of that. So make sure you post any questions you have or any problems you face. I want all my students to finish this course. Let’s get through this together.

Who this course is for:

All level of Data Science practitioners
All levels of Machine Learning practitioners
Those 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

Julia Programming Language - From Zero to Expert

What you'll learn

Explore related topics

Course content

Intro and Setting up3 lectures • 8min

Core Language Basics5 lectures • 30min

Arrays and Matrices: Native Language Support3 lectures • 19min

Functions and Fun Stuff5 lectures • 38min

Getting Started with Data Science3 lectures • 39min

Case Studies in Data Science3 lectures • 21min

Deep Learning - Flux in Julia6 lectures • 53min

Parting Words1 lecture • 4min

Update: Generative Adversarial Networks (GANs)5 lectures • 29min

Requirements

Description

Who this course is for: