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Supervised Learning

Master the most common type of machine learning. Learn to build models that make predictions based on labeled data, covering regression and classification tasks.

Supervised Learning

Practical Decision Trees with Scikit-Learn

Binary Classification in ML.NET: Practical Supervised Learning

Practical Random Forests: Build, Tune, and Evaluate Ensemble Models

Understanding the Tree-Structured Parzen Estimator for Hyperparameter Tuning

Feature Engineering: Retraining Logistic Regression without Dummy Variables

PySpark MLlib for Beginners: Predict Diabetes with Machine Learning

Supervised Learning Challenge: Predicting Customer Churn

Visualizing Machine Learning Models with Plotly

Hyperparameter Tuning with Grid Search and Logistic Regression

Parameter Estimation in Machine Learning: MLE, MAP, and Bayesian Inference

Regularization for Machine Learning: Lasso and Ridge Regression

Introduction to Ensemble Learning: Bagging and Boosting Explained

Introduction to Machine Learning Model Evaluation and Validation

Regularization Techniques: Ridge and Lasso Regression for Beginners

Random Forest Models in R with Tidymodels

Optimizing Logistic Regression with Random Search

Introduction to Ensemble Learning for Machine Learning Models

Polynomial Regression in Python: Modeling Non-Linear Relationships

Model Fairness and Bias Detection with SageMaker Clarify

Evaluating and Deploying SVM Models with ROC Curves

Multiclass Classification with ML.NET for Beginners

Maximum Likelihood Estimation: Statistical Parameter Fitting for Beginners

Machine Learning Model Evaluation and Selection

Linear Regression Fundamentals for Business Machine Learning