WalkSelf · AI, Tech & Engineering · AI & Machine Learning

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

Explainable AI: Interpreting Black Box Machine Learning Models

Tuning Decision Trees: Preventing Overfitting in Machine Learning

Predicting Probabilities with Logistic Regression in Python

Multiclass Logistic Regression: Foundations of Classification

Selecting and Formulating Problems for Machine Learning

Optimizing Random Forest Models with tidymodels in R

Visualizing Decision Trees and Random Forests in Python

Managing Imbalanced Datasets for Multiclass Classification

Classification Trees in R: Training with Gini Impurity

Introduction to Self-Supervised Learning: Working with Unlabeled Data

Ensemble Learning: Decision Trees, Bagging, and Random Forests

Introduction to Machine Learning: Core Concepts and Modern Workflows

Streamlining Machine Learning Workflows with Scikit-Learn

Selecting Supervised Learning Models: A Real Estate Case Study

Training Decision Tree Models Using the Gini Criterion

Training Random Forests: Entropy and Gini Criteria

Machine Learning with JAX: Foundations of High-Performance ML

Bayesian Regression for Software Engineering

Ensemble Learning with Bagging and Random Forests in Python

Linear Regression and Regularization Fundamentals with Python

Foundations of Classification in Supervised Machine Learning

Evaluating Bayesian Networks with ROC Curves and Penalty Heuristics

Machine Learning Model Selection: Baseline Accuracy, Logistic Regression, and KNN

Classification Model Accuracy Metrics in R