Julia Programming Language: From Zero to Hero

Julia Programming Language - From Zero to Hero

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

Created by : Rated :4.8 591 views

Last Updated: 2024-07-08 09:07:52 English Course Type: Self Learning

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)

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.

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

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

Section 1 : Introduction and Setting up 2 Lectures 00:08:17
Lecture 1 :
Installation of Julia Programming Preview
Lecture 2 :
Packages and Interactive Notebook
Section 2 : Core Language Basics 4 Lectures 00:29:33
Lecture 1 :
Basics Syntax , Variables & Operations
Lecture 2 :
Control Structures, Iterations and Ranges
Lecture 3 :
Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples
Lecture 4 :
Dictionaries ,Symbols in Julia
Section 3 : Arrays and Matrices: Native Language Support 2 Lectures 00:19:14
Lecture 1 :
Arrays
Lecture 2 :
Data Type, Casting Among Types
Section 4 : Functions and Fun Stuff 4 Lectures 00:37:43
Lecture 1 :
Defining Functions
Lecture 2 :
Anonymous Functions, Splatting and Slurping
Lecture 3 :
Functional Programming, Broadcasting
Lecture 4 :
Interfacing with Python and R
Section 5 : Getting Started with Data Science 3 Lectures 00:39:09
Lecture 1 :
Plotting Basics
Lecture 2 :
Data Wrangling, Reading CSV Files, Descriptive Case Study
Lecture 3 :
Further Data Manipulation, Apache Arrow, Grouping and analysis
Section 6 : Case Studies in Data Science 2 Lectures 00:20:32
Lecture 1 :
Case Study: Clustering for Housing/Map Data
Lecture 2 :
Classification with Decision Tree
Section 7 : Deep Learning - Flux in Julia 5 Lectures 00:53:12
Lecture 1 :
Writing a Natural Network From Scratch
Lecture 2 :
Multiple Layers, State-of-the-Art in a Few More Lines
Lecture 3 :
Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls
Lecture 4 :
MNIST Continued, Creating the Deep Model, Training and Testing
Lecture 5 :
Saving and Loading Models, Exploring More Options
Section 8 : Parting Words 1 Lectures 00:03:59
Lecture 1 :
Where to Go from Here: Pointers for Further Learning

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Dr. Mohammed Nauman,

4807 Course Views

9 Courses

I have a PhD in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. I have been programming since early 2000 and have worked with many different languages, tools and platforms. I have an extensive research experience with many state-of-the-art models to my name. My research in Android security has led to some major shifts in the Android permission model.