Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Understand how to work with vectors and matrices in Julia
• Learn about loading and plotting datasets in Julia

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Learn how to optimize a single hyperparameter of your model
• Learn how to tune multiple hyperparameters, that are possibly nested, and visualize the results

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Familiarize yourself with generating and visualizing custom classification data
• Learn how to build and tune support vector machine (SVM) models with MLJ

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Learn how to load tabular data, set up its scientific types and study any existing imbalance
• Observe how basic random oversampling can significantly improve decision tree performance on imbalanced data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Learn how to study the imbalance of an existing dataset
• Get a stronger grasp on how the ratios hyperparameter which reflects the amount of oversampling can affect the classification decision boundaries

Intended Learning Outcomes:

• Understand the relationship between pure random oversampling and the ROSE algorithm
• Understand the effect of increasing the `s` hyperparameter for ROSE

Intended Learning Outcomes:

• Observe how SMOTE can be used to address class imbalances on a real dataset with logistic regression as the classifier
• Familiarize yourself with common MLJ workflows related to loading and processing data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Use SMOTEN to address class imbalances on a real dataset with over 20 categorical columns
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Observe how SMOTENC can be used to address class imbalances on a real dataset with categorical and continuous columns
• Familiarize yourself with common MLJ workflows related to loading and processing data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Explore the effects of various hyperparameter(s) of the ENN algorithm and how it can be useful for data cleaning

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Understand how hybrid resampling algorithms such as SMOTE-Tomek can be defined with the `BalancedModel` construct

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Understand how balanced bagging can singifically improve classification performance on imbalanced data

Intended Learning Outcomes:

• Explore incremental training with MLJ

Intended Learning Outcomes:

• Learn how to tune different hyperparameters of MLJFlux models with emphasis on training hyperparameters.

Intended Learning Outcomes:

• Learn how to build and training neural networks for image classification

Intended Learning Outcomes:

• Learn how to train a neural network for spam classification over SMS messages

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Explore incremental training with MLJ

Intended Learning Outcomes:

• Learn how to tune different hyperparameters of MLJFlux models with emphasis on training hyperparameters.

Intended Learning Outcomes:

• Learn how to compose neural networks with other MLJ components

Intended Learning Outcomes:

• Learn how to compare neural networks with other models

Intended Learning Outcomes:

• Learn how early stopping can be applied to neural networks

Intended Learning Outcomes:

• Train neural networks and see learning plots in realtime

Intended Learning Outcomes:

• Learn how to naively search and compare different neural network architecture

Intended Learning Outcomes:

• Learn how to build and training neural networks for image classification

Intended Learning Outcomes:

• Learn how to train a neural network for spam classification over SMS messages

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Learn how to load tabular data, set up its scientific types and study any existing imbalance
• Observe how basic random oversampling can significantly improve decision tree performance on imbalanced data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Learn how to study the imbalance of an existing dataset
• Get a stronger grasp on how the ratios hyperparameter which reflects the amount of oversampling can affect the classification decision boundaries

Intended Learning Outcomes:

• Understand the relationship between pure random oversampling and the ROSE algorithm
• Understand the effect of increasing the `s` hyperparameter for ROSE

Intended Learning Outcomes:

• Observe how SMOTE can be used to address class imbalances on a real dataset with logistic regression as the classifier
• Familiarize yourself with common MLJ workflows related to loading and processing data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Use SMOTEN to address class imbalances on a real dataset with over 20 categorical columns
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Observe how SMOTENC can be used to address class imbalances on a real dataset with categorical and continuous columns
• Familiarize yourself with common MLJ workflows related to loading and processing data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Explore the effects of various hyperparameter(s) of the ENN algorithm and how it can be useful for data cleaning

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Understand how hybrid resampling algorithms such as SMOTE-Tomek can be defined with the `BalancedModel` construct

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Understand how balanced bagging can singifically improve classification performance on imbalanced data

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Learn how to optimize a single hyperparameter of your model
• Learn how to tune multiple hyperparameters, that are possibly nested, and visualize the results

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Familiarize yourself with generating and visualizing custom classification data
• Learn how to build and tune support vector machine (SVM) models with MLJ

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Have a grasp of how to build and analyze complex learning networks (e.g., stacking)
• Be able to evaluate and tune learning networks

Intended Learning Outcomes:

• Learn how to compare neural networks with other models

Intended Learning Outcomes:

• Learn how to naively search and compare different neural network architecture

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Familiarize yourself with common MLJ workflows related to loading and processing data
• Understand how hybrid resampling algorithms such as SMOTE-Tomek can be defined with the `BalancedModel` construct

Intended Learning Outcomes:

• Learn how to compose neural networks with other MLJ components

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Learn how to load tabular data, set up its scientific types and study any existing imbalance
• Observe how basic random oversampling can significantly improve decision tree performance on imbalanced data
• Practice MLJ workflows related to evaluation such as cross-validation and new metrics

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• Have a grasp of how to build and analyze complex learning networks (e.g., stacking)
• Be able to evaluate and tune learning networks

Intended Learning Outcomes:

• 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

Intended Learning Outcomes:

• 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