# Statistics and Machine Learning

## Introduction <a href="#documentation" id="documentation"></a>

Refer to <mark style="background-color:red;">Artificial Intelligence in Healthcare / Machine Learning Resources</mark> for general information about machine learning. This section provides instructions for getting started with some of the common statistics and machine learning Julia packages.

## Getting Started <a href="#getting_started" id="getting_started"></a>

* Start Julia REPL (Read/Evaluate/Print/Loop) by typing the following in Terminal or PowerShell:

```
$ julia
```

* Go into REPL mode for Pkg, Julia’s built in package manager, by pressing ‘]’:

```
julia> ]
(@v1.4) pkg>
```

* Update package repository in Pkg REPL

```
(@v1.4) pkg> update)
```

* Add packages in Pkg REPL

```
(@v1.4) pkg> add CSV
(@v1.4) pkg> add DataFrames
(@v1.4) pkg> add Distributions
(@v1.4) pkg> add HypothesisTests
```

* Get back to the Julia REPL and exit by pressing backspace or ^C

```julia
(@v1.4) pkg>
julia> using CSV
julia> using DataFrames
julia> using Distributions
julia> using HypothesisTests
julia> exit()
```

## Machine Learning Resources

<table><thead><tr><th width="171">Resource</th><th width="259">Relevant Pages</th><th>Time</th><th width="200">Access Requirements</th><th width="389">Notes</th></tr></thead><tbody><tr><td><a href="https://alan-turing-institute.github.io/MLJ.jl/dev/">MLJ Home</a></td><td><a href="https://alan-turing-institute.github.io/MLJ.jl/dev/about_mlj/">About</a></td><td>N/A</td><td>None</td><td>Language: Julia</td></tr><tr><td><a href="https://juliaai.github.io/DataScienceTutorials.jl/">Data Science Tutorials</a></td><td><a href="https://juliaai.github.io/DataScienceTutorials.jl/#getting_started_with_mlj">Getting Started with MLJ</a></td><td>N/A</td><td><p>None, Jupyter notebook for tutorials</p><p><br></p></td><td><p>Language: Julia</p><ul><li>10 steps/pages</li><li>Walk-through of process (choosing model; fit, predict, transform; tune; ensemble, etc.)</li></ul></td></tr><tr><td><a href="https://juliaai.github.io/DataScienceTutorials.jl/#introduction_to_statistical_learning_with_mlj">Introduction to Statistical Learning with MLJ</a></td><td>N/A</td><td>None, Jupyter notebook for tutorials</td><td><p>Language: Julia</p><ul><li>8 labs</li><li>e.g. <a href="https://juliaai.github.io/DataScienceTutorials.jl/isl/lab-3/">Lab 3 Linear Regression</a></li></ul></td><td></td></tr><tr><td><a href="https://juliaai.github.io/DataScienceTutorials.jl/#end_to_end_examples_with_mlj">End to end examples with MLJ</a></td><td>N/A</td><td>None, Jupyter notebook for tutorials</td><td><p>Language: Julia</p><ul><li>11 total, 9 simple</li><li>e.g. <a href="https://juliaai.github.io/DataScienceTutorials.jl/end-to-end/breastcancer/">Breast Cancer Wisconsin</a> </li></ul></td><td></td></tr><tr><td><a href="https://www.youtube.com/watch?v=qSWbCn170HU">JuliaCon 2020</a> (YouTube video)</td><td><a href="https://github.com/ablaom/MachineLearningInJulia2020">Follow-along Tutorials</a></td><td>~ 3.5 hrs</td><td>None</td><td><p>Language: Julia</p><p><br></p></td></tr></tbody></table>

## Resources <a href="#documentation" id="documentation"></a>

* <mark style="background-color:red;">Artificial Intelligence in Healthcare / Machine Learning Resources</mark>
* [Julia Cheat Sheet](https://juliadocs.github.io/Julia-Cheat-Sheet/)
* [Julia Stats](http://juliastats.github.io/)
* Packages
  * [CSV.jl](https://juliadata.github.io/CSV.jl/stable/)
  * [DataFrames.jl](https://dataframes.juliadata.org/stable/)
  * [Distributions.jl](https://juliastats.org/Distributions.jl/latest/)
  * [HypothesisTests.jl](http://juliastats.github.io/HypothesisTests.jl/dev/)
* [Statistics Module](https://docs.julialang.org/en/v1/stdlib/Statistics/index.html)
* [Random Number Generator](https://docs.julialang.org/en/v1/stdlib/Random/index.html)


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