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🚑 Move two tutorials
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_libs/nav/head.js

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@@ -238,16 +238,7 @@ const navItems = [
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"Understand the different types and methods introduced by MLJ",
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]
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},
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{
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name: "KNN & Ridge Regression Learning Network on AMES Pricing Data",
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href: "/end-to-end/AMES/",
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tags: ["Regression", "Learning Networks", "Hyperparameter Tuning"],
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ilos: [
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"Get familiar with building baselines models for your machine learning task",
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"Learn how to build simple learning networks (advanced model composition) in MLJ",
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"Learn how to tune and analyze the evaluation results from learning networks"
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]
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},
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{
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name: "KNN, Logistic Regression and PCA on Wine Dataset",
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href: "/end-to-end/wine/",
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]
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},
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{
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name: "Linear Regression on Temporal Power Generation Data",
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name: "Linear Regression on Temporal Power Data",
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href: "/end-to-end/powergen/",
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tags: ["Data Processing", "Regression"],
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ilos: [
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]
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},
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{
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name: "Benchmarking Classification Models on Breast Cancer Data",
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href: "/end-to-end/breastcancer",
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tags: [
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"Encoders",
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"Classification",
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"Iterative Models",
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"Distribution Fitter",
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"Bayesian Models",
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"Neural Networks",
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],
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ilos: [
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"Familiarize yourself with common data preprocessing and visualization workflows",
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"Learn how MLJ can be used to benchmark a large set of models against some dataset"
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]
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},
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{
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name: "Credit Fraud Detection with Logistic Regression, SVM and Neural Networks",
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name: "Credit Fraud Detection with Classical and Deep Models",
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href: "/end-to-end/creditfraud",
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tags: [
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"Classification",
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id: "advanced",
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href: "#!",
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sections: [
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{
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name: "Benchmarking Classification Models on Breast Cancer Data",
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href: "/advanced/breastcancer",
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tags: [
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"Encoders",
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"Classification",
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"Iterative Models",
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"Distribution Fitter",
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"Bayesian Models",
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"Neural Networks",
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],
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ilos: [
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"Familiarize yourself with common data preprocessing and visualization workflows",
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"Learn how MLJ can be used to benchmark a large set of models against some dataset"
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]
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},
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{
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name: "KNN & Ridge Regression Learning Network on AMES Pricing Data",
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href: "/advanced/AMES/",
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tags: ["Regression", "Learning Networks", "Hyperparameter Tuning"],
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ilos: [
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"Get familiar with building baselines models for your machine learning task",
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"Learn how to build simple learning networks (advanced model composition) in MLJ",
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"Learn how to tune and analyze the evaluation results from learning networks"
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]
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},
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{
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name: "Build Basic Learning Networks with MLJ",
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href: "/advanced/ensembles-3",

_literate/end-to-end/AMES/tutorial.jl renamed to _literate/advanced/AMES/tutorial.jl

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using Pkg # hideall
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Pkg.activate("_literate/end-to-end/AMES/Project.toml")
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Pkg.activate("_literate/advanced/AMES/Project.toml")
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Pkg.instantiate()
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# Build a model for the Ames House Price data set using a simple learning network to blend

_literate/end-to-end/breastcancer/tutorial.jl renamed to _literate/advanced/breastcancer/tutorial.jl

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using Pkg # hideall
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Pkg.activate("_literate/end-to-end/breastcancer/Project.toml")
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Pkg.activate("_literate/advanced/breastcancer/Project.toml")
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Pkg.instantiate()
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macro OUTPUT()
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return isdefined(Main, :Franklin) ? Franklin.OUT_PATH[] : "/tmp/"

end-to-end/AMES.md renamed to advanced/AMES.md

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# AMES
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\tutorial{end-to-end/AMES}
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\tutorial{advanced/AMES}

end-to-end/breastcancer.md renamed to advanced/breastcancer.md

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# Breast Cancer Wisconsin(Diagnostic)
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\tutorial{end-to-end/breastcancer}
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\tutorial{advanced/breastcancer}

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