21 lines
1.7 KiB
Markdown
21 lines
1.7 KiB
Markdown
# Introduction to Statistical Learning with Julia
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## Introduction
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[An Introduction to Statistical Learning: With Applications in R](http://faculty.marshall.usc.edu/gareth-james/ISL/) is a great book to learn data science. The associated code is [R](https://www.r-project.org/about.html) which is a nice programming language for statisticians. However, R is not fast and in my opinion, it does not have nice syntax. There is a project named [ISLR-python](https://github.com/JWarmenhoven/ISLR-python) which ports the book to Python. I was inspired by the Python project and try to implement the introduced materials of this book in [Julialang](https://julialang.org/). Julia is a new language which is faster and more friendly to scientific computing than Python. Hopefully, this code is helpful for Julia lovers when they read the book.
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## Library
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In this project, I use Julia v1.5.1 and the following library
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- [Plots.jl](https://github.com/JuliaPlots/PlotDocs.jl) for visualization
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- [CSV.jl](https://csv.juliadata.org/v0.4.0/) for reading csv file
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- [Clustering.jl](https://github.com/JuliaStats/Clustering.jl) for clustering algorithms
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- [LIBSVM.jl](https://github.com/JuliaML/LIBSVM.jl) for LibSVM implementation
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- [StatsPlots.jl](https://github.com/JuliaPlots/StatsPlots.jl) for plotting statistic
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- [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) for data frame
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- [GLM.jl](https://github.com/JuliaStats/GLM.jl) for generalized linear model
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- [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) for stat distributions
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## Note
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I write a [blog post](http://tndoan.com/2020/12/25/lessons-learned-from-islr/) to summarize the lessons that I learn after doing this project.
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