https://github.com/kgosiruri/learning-julia
This repository documents my journey learning Julia, a high-performance programming language designed for technical computing. It includes practice scripts, example projects, and notes on core Julia concepts such as syntax, data structures, performance optimization, and working with packages.
https://github.com/kgosiruri/learning-julia
ai julia machine-learning
Last synced: 2 months ago
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This repository documents my journey learning Julia, a high-performance programming language designed for technical computing. It includes practice scripts, example projects, and notes on core Julia concepts such as syntax, data structures, performance optimization, and working with packages.
- Host: GitHub
- URL: https://github.com/kgosiruri/learning-julia
- Owner: kgosiruri
- Created: 2025-06-04T20:19:39.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-07T15:41:25.000Z (about 1 year ago)
- Last Synced: 2025-06-07T16:41:02.204Z (about 1 year ago)
- Topics: ai, julia, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 22.8 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📘 Learning Julia with Jupyter
**Author:** Kgosi Ruri Molebatsi
**Notebook:** `full.ipynb`
This repository contains a Jupyter Notebook written using the Julia language via the IJulia kernel. It follows exercises and concepts from the *Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence* textbook.
---
## 📌 Purpose
The notebook is a hands-on exploration of Julia's capabilities in:
- Basic scripting and control flow
- Data science essentials
- Visualization
- Mathematical functions and statistics
- Package management and plotting
---
## 🚀 Getting Started
### 📦 Prerequisites
Ensure you have [Julia](https://julialang.org/downloads/) installed and install the IJulia package:
```julia
using Pkg
Pkg.add("IJulia")
```
Also, install required packages:
```
Pkg.add.(["Random", "Plots", "Statistics", "Roots", "LinearAlgebra", "StatsBase",
"LaTeXStrings", "Measures", "Images", "HTTP", "JSON"])
```
## 🧪 Running the Notebook:
1. Start Jupyter Notebook from your terminal or Anaconda.
2. Ensure you select the Julia kernel (IJulia).
3. Open and run full 2.ipynb.
## 🔍 Features Used:
- println, for loops, array comprehensions
- Math operations (e.g., squaring, square roots)
- Use of Julia’s package system (Pkg.add)
- Plotting and visual presentation via Plots.jl
## 📊 Example Outputs
This notebook includes practical examples and outputs from exercises based on the textbook:
**_Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence_**
Below are some sample outputs generated during exploration:
### ✅ Descriptive Statistics
- Summary statistics, histograms, and boxplots
```julia
julia> describe(randn(100))
```
## 🎓 Learning Resource
This notebook is built as a personal exercise space to practice topics from the textbook:
Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence