Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence
This repository is a collection of all 200+ code blocks contained in the book. See the Book's website, or go directly to Springer:
The book is comprised of the following ten chapters and three appendices:
- Introducing Julia
- Basic Probability
- Probability Distributions
- Processing and Summarizing Data
- Statistical Inference Concepts
- Confidence Intervals
- Hypothesis Testing
- Linear Regression and Extensions
- Machine Learning Basics
- Simulation of Dynamic Models
- How-to in Julia
- Additional Language Features
- Additional Packages
- Clone or download this repository or a fork of it.
- Have Julia 1.4 or above installed.
- Run init.jl to install and precompile the required packages.
- Run individual code examples.
An alternative is to use Pluto. See StatisticsWithJuliaPlutoNotebooks.jl
We hope you find this an enjoyable and instructive resource.