Julia Programming Language - Video Course

What You Will Get

Video lecture covering:   

• Syntax of Julia (and differences from Python)
• Strength 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 learning
• Create ML models from scratch in a way that helps you make modifications easily
• Lifetime access to materials. So, feel free to cancel the subscription after you're done with the course. 

Plus: Excellent support answering any queries you might have. 

Course Description: 

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.

Video Contents: 

Section 0: Intro and Setting up

1. Installing Julia (Windows, Linux and MacOS)

2. Packages and Interactive Notebook

Section 1: 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), Symbols in Julia

Section 2: Arrays and Matrices: Native Language Support

1. Arrays, Matrices, Tensors, Reshaping, Helper Functions

2. Data Type Details, Casting Among Types

Section 3:  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

Section 4:  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

Section 5: Case Studies in Data Science

1. Case Study: Clustering for Housing/Map Data

2. Classification with Decision Trees/Random Forests

Section 6: 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

Section 7: Parting Words

1. Where to Go from Here: Pointers for Further Learning 

Remember: If you have any queries, contact me through the given support email and I'll get back to you within 24 hours!