diff --git a/_css/nav.css b/_css/nav.css index 3153bca5..edc481d3 100644 --- a/_css/nav.css +++ b/_css/nav.css @@ -44,7 +44,6 @@ a#home, a#contribute { justify-content: space-around; } nav { - //float: right; display: flex; justify-content: space-around; padding: 0.5rem; @@ -78,7 +77,6 @@ nav ul li { nav ul li a, nav ul li a:visited { display: block; - //padding: 0 20px; line-height: 50px; background-color: #f1f1f1; color: #363636; @@ -112,6 +110,10 @@ nav ul li a:visited:not(:only-child):after { nav ul li ul li a { padding: 15px; line-height: 20px; + font-weight: 300; +} +nav ul li ul li { + border-bottom: 1px solid #00000021; } .nav-dropdown { position: absolute; @@ -135,7 +137,7 @@ nav ul li ul li a { .bottom-nav { background-color: #f1f1f1; font-weight: 600; - padding: 8px 8px; + padding: 15px 15px; color: #2e2e2e; border-radius: 45px; outline: none; @@ -146,8 +148,9 @@ nav ul li ul li a { flex-direction: column; align-items: center; text-decoration: none; - width: 200px; + max-width: 500px; border: 2px solid #c1c1c1; + } .bottom-nav:hover { background-color: #9b59b6; diff --git a/_css/side-menu.css b/_css/side-menu.css index 874cf531..9feee464 100644 --- a/_css/side-menu.css +++ b/_css/side-menu.css @@ -6,6 +6,12 @@ body { .header h2 { text-align: center; + +} + +#menu-logo-link div { + white-space: normal !important; + /* word-wrap: normal !important; */ } .pure-img-responsive { @@ -65,6 +71,7 @@ The content `
` is where all your content goes. margin: 0.2em 0; font-size: 3em; font-weight: 300; + } .header h1::before { content: url('https://seeklogo.com/images/J/julia-logo-DC9698BAF9-seeklogo.com.png'); @@ -122,6 +129,8 @@ appears on the left side of the page. color: #999; border: none; padding: 0.6em 0 0.6em 0.6em; + word-wrap: break-word; + white-space: break-spaces; } /* diff --git a/_libs/nav/head.js b/_libs/nav/head.js index 968b485e..36a69cac 100644 --- a/_libs/nav/head.js +++ b/_libs/nav/head.js @@ -6,37 +6,54 @@ const navItems = [ href: "/info/data", sections: [ { - name: "Choosing a model", - href: "/getting-started/choosing-a-model/", + name: "Loading and Accessing Data", + href: "/data/loading/", tags: ["Data Processing"], + ilos: [ + "Understand how to load and access various datasets in R using RDatasets.jl", + "Learn how to save and load a local dataset in CSV format using CSV.jl" + ] }, { - name: "Fit, predict, transform", - href: "/getting-started/fit-and-predict/", + name: "Manipulating Data Frames with DataFrames.jl", + href: "/data/dataframe/", tags: ["Data Processing"], + ilos: [ + "Learn how to inspect, describe, and convert datasets into the form of Data Frames", + "Learn how to modify a Data Frame by adding columns and imputing missing values", + "Familiarize yourself with the groupby and combine operations on Data Frames" + ] }, { - name: "Model tuning", - href: "/getting-started/model-tuning/", + name: "Working with Categorical Data", + href: "/data/categorical/", tags: ["Data Processing"], + ilos: [ + "Understand the different types of categorical data (e.g., nominal and ordinal data) via CategoricalArrays.jl", + "Learn how to work with and utilize such categorical arrays" + ] }, { - name: "Ensembles", - href: "/getting-started/ensembles/", + name: "Understanding Scientific Types", + href: "/data/scitype/", tags: ["Data Processing"], + ilos: [ + "Gain a comprehension of the rationale behind having scientific types and their different categories", + "Learn how to inspect and modify the scientific types in your data using ScientificTypes.jl", + "Learn about practical tips and tricks related to scientific types" + ] }, { - name: "Ensembles (2)", - href: "/getting-started/ensembles-2/", + name: "Data Processing and Visualization", + href: "/data/processing/", tags: ["Data Processing"], - }, - { - name: "Composing models", - href: "/getting-started/composing-models/", - tags: ["Data Processing", "Missing Value Imputation"], + ilos: [ + "Learn how to apply common data processing techniques on a real-world dataset", + "Learn how to create various plots (e.g., bar charts and histograms) to analyze your data" + ] }, ], - sectionItemWidth: "short-item", + sectionItemWidth: "long-item", }, { name: "Getting Started", @@ -44,37 +61,66 @@ const navItems = [ href: "/info/getting-started", sections: [ { - name: "Choosing a model", + name: "Preparing data and model with Iris", href: "/getting-started/choosing-a-model/", tags: ["Classification", "Regression"], + ilos: [ + "Understand why and how to coerce the data types of different variables in your dataset", + "Learn how to separate features and targets for training", + "Be able to find and load the models suitable for your data" + ] }, { - name: "Fit, predict, transform", + name: "Supervised and Unsupervised Workflows in MLJ", href: "/getting-started/fit-and-predict/", tags: ["Classification", "Encoders"], + ilos: [ + "Learn how to implement a supervised learning workflow with MLJ", + "Learn how to implement an unsupervised learning workflow with MLJ", + "Familiarize yourself with using MLJ's classification and transformation models", + ] }, { - name: "Model tuning", + name: "Hyperparameter Tuning for Single and Composite Models", href: "/getting-started/model-tuning/", tags: ["Classification", "Hyperparameter Tuning"], + ilos: [ + "Learn how to optimize a single hyperparameter of your model", + "Learn how to tune multiple hyperparameters, that are possibly nested, and visualize the results" + ] }, { - name: "Ensembles", + name: "Building and Tuning Bagging Ensemble Models", href: "/getting-started/ensembles/", tags: ["Regression", "Ensemble Models", "Hyperparameter Tuning"], + ilos: [ + "Understand how to implement bagging ensemble models in MLJ and compare them to atomic models", + "Learn how to optimize the parameters of bagging ensemble models and visualize the results" + ] }, { - name: "Ensembles (2)", + name: "Building Random Forests with Bagging Ensembles", href: "/getting-started/ensembles-2/", tags: ["Regression", "Ensemble Models", "Hyperparameter Tuning"], + ilos: [ + "Familiarize yourself with dealing with real-world datasets such as the Boston Housing dataset", + "Understand how to implement Random Forests using bagging over Decision Trees", + "Learn how to analyze the effect of a specific hyperparameter using MLJ's learning curve", + "Learn how to tune the parameters of Random Forests" + ] }, { - name: "Composing models", + name: "Composing Models and Target Transformations", href: "/getting-started/composing-models/", tags: ["Regression", "Encoders", "Pipelines"], + ilos: [ + "Learn how to transform the target of your regression data using MLJ", + "Understand how to combine models and transformation algorithms in MLJ", + "Gain an understanding of the benefits of using MLJ pipelines" + ] }, ], - sectionItemWidth: "medium-item", + sectionItemWidth: "long-item", }, { name: "Intro to Stats Learning", @@ -82,40 +128,81 @@ const navItems = [ href: "/info/isl", sections: [ { - name: "Basic Operations", + name: "Vectors, Matrices and Data Loading in Julia", href: "/isl/lab-2/", tags: ["Data Processing"], - }, - { name: "Linear Regression", href: "/isl/lab-3/", tags: ["Regression"] }, - { - name: "Logistic Regression & Friends", + ilos : [ + "Understand how to work with vectors and matrices in Julia", + "Learn about loading and plotting datasets in Julia" + ] + }, + { name: "Multivariate Linear Regression & Interactions", + href: "/isl/lab-3/", + tags: ["Regression"], + ilos: [ + "Understand how to build single and multivariable linear regression models with MLJ", + "Learn how to add interaction terms to model nonlinear trends in your data", + "Learn how to plot regression fits and their residuals" + ] + }, + { + name: "Logistic Regression & Friends on Stock Market Data", href: "/isl/lab-4/", tags: ["Classification", "Bayesian Models", "Distribution Fitter"], + ilos: [ + "Understand how to load and preprocess example datasets from RDatasets.jl", + "Explore how to train and analyze logistic regression on stock market data", + "Explore classification-related metrics such as cross-entropy loss, confusion matrix, and area under the ROC curve", + "Compare logistic regression to various other classifiers such as LDA, QDA, and KNN", + "Analyze training classification models on imbalanced datasets", + ] }, { - name: "Cross Validation", + name: "Building Polynomial Regression Models and Tuning Them", href: "/isl/lab-5/", tags: ["Regression", "Feature Selection", "Hyperparameter Tuning"], + ilos: [ + "Understand how to build a polynomial regression model with MLJ", + "Learn how to use feature selectors and models in an MLJ pipeline", + "Analyze and hyperparameter tune polynomial regression models" + ] }, { - name: "Ridge & Lasso Regression", + name: "Ridge & Lasso Regression on Hitters Dataset", href: "/isl/lab-6b/", tags: ["Regression", "Encoders", "Hyperparameter Tuning"], + ilos: [ + "Strengthen your data preparation, plotting, and analysis skills", + "Compare different types of linear regression such as Lasso and Ridge regression", + "Refresh on hyperparameter tuning and model composition with MLJ " + ] }, { - name: "Tree-based Models", + name: "Exploring Tree-based Models", href: "/isl/lab-8/", - tags: ["Iterative Models", "Classification", "Regression"], + tags: ["Iterative Models", "Classification", "Regression", "Hyperparameter Tuning"], + ilos: [ + "Explore various tree-based models for classification and regression including ordinary decision trees, random forests, and XGBoost", + "Refresh your skills on hyperparameter tuning and building MLJ pipelines" + ] }, { - name: "Support Vector Machine", + name: "Building and Tuning a Support Vector Machine", href: "/isl/lab-9/", tags: ["Classification", "Hyperparameter Tuning"], + ilos: [ + "Familiarize yourself with generating and visualizing custom classification data", + "Learn how to build and tune support vector machine (SVM) models with MLJ" + ] }, { - name: "PCA & Clustering", + name: "Unsupervised Learning with PCA and Clustering ", href: "/isl/lab-10/", tags: ["Dimensionality Reduction", "Clustering", "Pipelines"], + ilos: [ + "Learn how to build unsupervised models such as KMeans and PCA in MLJ", + "Learn how to analyze and visualize results from unsupervised models such as KMeans and PCA" + ] }, ], sectionItemWidth: "long-item", @@ -127,7 +214,7 @@ const navItems = [ href: "/info/end-to-end", sections: [ { - name: "Telco Churn", + name: "MLJ for Data Scientists in Two Hours", href: "/end-to-end/telco/", tags: [ "Classification", @@ -137,14 +224,23 @@ const navItems = [ "Iterative Models", "Hyperparameter Optimization", ], + ilos: [ + "Get a grasp on using MLJ as a data scientist new to MLJ or Julia", + "Refresh your skills on building simple models", + "Learn how to prepare example real-life data by loading, coercing, partitioning and unpacking data", + "Learn how to build pipelines in MLJ", + "Learn about how to manually and automatically evaluate models in MLJ", + "Understand how to perform feature selection in MLJ", + "Learn how to wrap models in iterative strategies in MLJ", + "Learn how to tune hyperparameters in MLJ", + "Familiarize yourself with confusion matrices, ROC curve and stratified cross-validation", + "Learn how to save and perform final evaluations on your models in MLJ", + "Understand the different types and methods introduced by MLJ", + ] }, + { - name: "AMES", - href: "/end-to-end/AMES/", - tags: ["Regression", "Learning Networks", "Hyperparameter Tuning"], - }, - { - name: "Wine", + name: "KNN, Logistic Regression and PCA on Wine Dataset", href: "/end-to-end/wine/", tags: [ "Encoders", @@ -152,14 +248,24 @@ const navItems = [ "Pipelines", "Dimensionality Reduction", ], + ilos: [ + "Familiarize yourself with the common data preprocessing steps in MLJ", + "Refresh your skills on building pipelines and comparing classification models with MLJ", + "Learn how to reduce the dimensionality of high-dimensional data using dimensionality reduction techniques such as PCA" + ] }, { - name: "Crabs (XGB)", + name: "XGBoost on Crabs Dataset", href: "/end-to-end/crabs-xgb/", tags: ["Classification", "Iterative Models", "Hyperparameter Tuning"], + ilos: [ + "Learn how to build XGBoost models in MLJ", + "Familiarize yourself with various XGBoost hyperparameters and their effects", + "Refresh your skills on using learning curves and hyperparameter tuning in MLJ" + ] }, { - name: "Horse", + name: "EvoTree Classifier on Horse Colic Dataset", href: "/end-to-end/horse/", tags: [ "Missing Value Imputation", @@ -168,34 +274,62 @@ const navItems = [ "Iterative Models", "Hyperparameter Tuning", ], + ilos: [ + "Familiarize yourself with common data preprocessing techniques in Julia", + "Get familiar with building baselines models for your learning task in MLJ", + "Refresh your understanding of using pipelines, evaluation and hyperparameter tuning in MLJ" + ] }, { - name: "King County Houses", + name: "Tree-based models on King County Houses Dataset", href: "/end-to-end/HouseKingCounty/", tags: ["Regression", "Iterative Models"], + ilos: [ + "Familiarize yourself with common data preprocessing and visualization techniques in Julia", + "Explore different tree-based models such as decision trees, random forests and gradient boosters and compare them together" + ] }, { - name: "Airfoil", + name: "Tree-based models on Airfoil Dataset", href: "/end-to-end/airfoil", tags: ["Encoders", "Regression", "Hyperparameter Tuning"], + ilos: [ + "Familiarize yourself with common data preprocessing and visualization techniques in Julia", + "Explore different tree-based models such as decision trees, random forests and compare them together", + "Refresh your understanding of tuning hyperparameters with MLJ and analyzing tuning results" + ] }, { - name: "Boston (lgbm)", + name: "LightGBM on Boston Data", href: "/end-to-end/boston-lgbm", tags: ["Regression", "Hyperparameter Tuning", "Iterative Models"], + ilos: [ + "Familiarize yourself with common data preprocessing and visualization techniques in Julia", + "Build and analyze LightGBM models in MLJ by utilizing learning curves and hyperparameter tuning", + ] }, { - name: "Using GLM.jl", + name: "Exploring Generative Linear Models", href: "/end-to-end/glm/", - tags: ["Pipelines", "Encoders", "Classification"], + tags: ["Pipelines", "Encoders", "Classification", "Regression"], + ilos: [ + "Understand how to use generative linear models from GLM.jl in MLJ", + "Practice examples of using linear regression and logistic regression models in MLJ", + "Understand how to interpret the outputs from linear and logistic regression models" + ] }, { - name: "Power Generation", + name: "Linear Regression on Temporal Power Data", href: "/end-to-end/powergen/", tags: ["Data Processing", "Regression"], + ilos: [ + "Familiarize yourself with common data preprocessing and visualization workflows", + "Gain an understanding of exploratory data analytics to better understand the data before developing your model", + "Train and analyze linear regression models on temporal data with MLJ" + ] }, { - name: "Boston (Flux)", + name: "Custom Neural Networks on Boston Data", href: "/end-to-end/boston-flux", tags: [ "Neural Networks", @@ -203,21 +337,13 @@ const navItems = [ "Regression", "Iterative Models", ], + ilos: [ + "Learn how to build and train arbitrary feedforward neural networks via MLJFlux.jl", + "Understand how deep learning MLJFlux models can be hyperparameter tuned with MLJ" + ] }, { - name: "Breast Cancer", - href: "/end-to-end/breastcancer", - tags: [ - "Encoders", - "Classification", - "Iterative Models", - "Distribution Fitter", - "Bayesian Models", - "Neural Networks", - ], - }, - { - name: "Credit Fraud", + name: "Credit Fraud Detection with Classical and Deep Models", href: "/end-to-end/creditfraud", tags: [ "Classification", @@ -226,6 +352,13 @@ const navItems = [ "Pipelines", "Neural Networks", ], + ilos: [ + "Familiarize yourself with common data preprocessing and visualization workflows", + "Refresh your understanding of classification metrics such as the confusion matrix and ROC curves", + "Build and hyperparameter tune logistic regression and SVM models", + "Learn how to build basic neural networks with MLJFlux.jl", + "Learn how to correct for class imbalance using the Imbalance.jl package" + ] }, ], sectionItemWidth: "long-item", @@ -236,14 +369,49 @@ const navItems = [ href: "#!", sections: [ { - name: "Ensembles (3)", + name: "Benchmarking Classification Models on Breast Cancer Data", + href: "/advanced/breastcancer", + tags: [ + "Encoders", + "Classification", + "Iterative Models", + "Distribution Fitter", + "Bayesian Models", + "Neural Networks", + ], + ilos: [ + "Familiarize yourself with common data preprocessing and visualization workflows", + "Learn how MLJ can be used to benchmark a large set of models against some dataset" + ] + }, + { + name: "KNN & Ridge Regression Learning Network on AMES Pricing Data", + href: "/advanced/AMES/", + tags: ["Regression", "Learning Networks", "Hyperparameter Tuning"], + ilos: [ + "Get familiar with building baselines models for your machine learning task", + "Learn how to build simple learning networks (advanced model composition) in MLJ", + "Learn how to tune and analyze the evaluation results from learning networks" + ] + }, + { + name: "Build Basic Learning Networks with MLJ", href: "/advanced/ensembles-3", tags: ["Regression", "Learning Networks"], + ilos: [ + "Have a clear understanding of how learning networks function in MLJ", + "Be able to construct basic learning networks with MLJ", + "Understand how to evaluate and tune learning networks" + ] }, { - name: "Stacking", + name: "Stacking with Learning Networks", href: "/advanced/stacking/", - tags: ["Ensemble Models", "Learning Networks", "Hyperparamter Tuning"], + tags: ["Ensemble Models", "Learning Networks", "Hyperparameter Tuning"], + ilos: [ + "Have a grasp of how to build and analyze complex learning networks (e.g., stacking)", + "Be able to evaluate and tune learning networks" + ] }, ], sectionItemWidth: "medium-item", diff --git a/_literate/end-to-end/AMES/Manifest.toml b/_literate/advanced/AMES/Manifest.toml similarity index 100% rename from _literate/end-to-end/AMES/Manifest.toml rename to _literate/advanced/AMES/Manifest.toml diff --git a/_literate/end-to-end/AMES/Project.toml b/_literate/advanced/AMES/Project.toml similarity index 100% rename from _literate/end-to-end/AMES/Project.toml rename to _literate/advanced/AMES/Project.toml diff --git a/_literate/end-to-end/AMES/tutorial.jl b/_literate/advanced/AMES/tutorial.jl similarity index 99% rename from _literate/end-to-end/AMES/tutorial.jl rename to _literate/advanced/AMES/tutorial.jl index 8e209d46..fc12095c 100644 --- a/_literate/end-to-end/AMES/tutorial.jl +++ b/_literate/advanced/AMES/tutorial.jl @@ -1,5 +1,5 @@ using Pkg # hideall -Pkg.activate("_literate/end-to-end/AMES/Project.toml") +Pkg.activate("_literate/advanced/AMES/Project.toml") Pkg.instantiate() # Build a model for the Ames House Price data set using a simple learning network to blend diff --git a/_literate/end-to-end/breastcancer/Manifest.toml b/_literate/advanced/breastcancer/Manifest.toml similarity index 100% rename from _literate/end-to-end/breastcancer/Manifest.toml rename to _literate/advanced/breastcancer/Manifest.toml diff --git a/_literate/end-to-end/breastcancer/Project.toml b/_literate/advanced/breastcancer/Project.toml similarity index 100% rename from _literate/end-to-end/breastcancer/Project.toml rename to _literate/advanced/breastcancer/Project.toml diff --git a/_literate/end-to-end/breastcancer/tutorial.jl b/_literate/advanced/breastcancer/tutorial.jl similarity index 99% rename from _literate/end-to-end/breastcancer/tutorial.jl rename to _literate/advanced/breastcancer/tutorial.jl index 84dc7c68..d497a3c6 100644 --- a/_literate/end-to-end/breastcancer/tutorial.jl +++ b/_literate/advanced/breastcancer/tutorial.jl @@ -1,5 +1,5 @@ using Pkg # hideall -Pkg.activate("_literate/end-to-end/breastcancer/Project.toml") +Pkg.activate("_literate/advanced/breastcancer/Project.toml") Pkg.instantiate() macro OUTPUT() return isdefined(Main, :Franklin) ? Franklin.OUT_PATH[] : "/tmp/" diff --git a/end-to-end/AMES.md b/advanced/AMES.md similarity index 64% rename from end-to-end/AMES.md rename to advanced/AMES.md index 2d1fafc5..3b3c978d 100644 --- a/end-to-end/AMES.md +++ b/advanced/AMES.md @@ -3,4 +3,4 @@ # AMES -\tutorial{end-to-end/AMES} +\tutorial{advanced/AMES} diff --git a/end-to-end/breastcancer.md b/advanced/breastcancer.md similarity index 69% rename from end-to-end/breastcancer.md rename to advanced/breastcancer.md index 462bd979..25d86592 100644 --- a/end-to-end/breastcancer.md +++ b/advanced/breastcancer.md @@ -3,4 +3,4 @@ # Breast Cancer Wisconsin(Diagnostic) -\tutorial{end-to-end/breastcancer} +\tutorial{advanced/breastcancer}