Curriculum
8 Sections
23 Lessons
4 Hours
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Intro & Setting Up
2
2.1
Installing Julia
3 Minutes
2.2
Packages and Interactive Notebook
5 Minutes
Core Language Basics
4
3.1
Basic Syntax, Variables & Operations
11 Minutes
3.2
Control Structures, Iterations and Ranges
4 Minutes
3.3
Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples
10 Minutes
3.4
Dictionaries (Maps), Symbols in Julia
4 Minutes
Arrays & Matrices: Native Language Support
2
4.1
Arrays, Matrices, Tensors, Reshaping, Helper Functions
16 Minutes
4.2
Data Type Details, Casting Among Types
3 Minutes
Functions & Fun Stuff
4
5.1
Defining Functions, Overloading, Multiple – Dispatch
12 Minutes
5.2
Anonymous Functions (and their importance), Splatting and Slurping
10 Minutes
5.3
Functional Programming, Broadcasting – Most Important Concept in Julia
11 Minutes
5.4
Interfacing with Python and R
4 Minutes
Getting Started with data science
3
6.1
Plotting Basics
5 Minutes
6.2
Data Wrangling, Reading CSV Files, Descriptive Case Study
14 Minutes
6.3
Further Data Manipulation, Apache Arrow, Grouping and analysis
19 Minutes
Case Studies in Data Science
2
7.1
Case Study: Clustering for Housing/Map Data
10 Minutes
7.2
Classifications with Decision Tree/ Random Forests
10 Minutes
Deep Learning - Flux in Julia
5
8.1
Writing a Neural Network from scratch in a few lines
16 Minutes
8.2
Multiple Layers, State-of-the-Art in a Few More Lines
8 Minutes
8.3
Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls
14 Minutes
8.4
MNIST Continued, Creating the Deep Model, Training and Testing
9 Minutes
8.5
Saving and Loading Models, Exploring More Options
5 Minutes
Parting Words
1
9.1
Where to Go from Here: Pointers for Further Learning
4 Minutes
Julia Programming Language – From Zero to Expert
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