PickAClass · 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

Hands-on Linear Regression: Coding Your First Predictive Models

Lasso Regression and Feature Selection in Python

Data Preprocessing for Machine Learning Competitions

Machine Learning Model Design Fundamentals

Introduction to Machine Learning: Core Algorithms and Paradigms

Feature Engineering for Ad Prediction Systems

Foundations of Multiple Linear Regression and Bias in Machine Learning

Evaluating Machine Learning Model Performance and Errors

Logistic Regression for Binary Classification on MNIST

Evaluating Binary Classifiers: ROC and Precision-Recall in PyTorch

Filtering Inappropriate Search Results with Machine Learning

Binary Classification with Logistic Regression and Gradient Descent

Implementing Logistic Regression for Binary Classification in Python

Decision Trees for Machine Learning with Python

Decision Tree Models: Classification and Regression with Scikit-Learn

Evaluating Logistic Regression Models with Python

Practical Machine Learning Best Practices for Beginners

Implementing Gradient Boosting Classifiers in Python

Applied Random Forests: From Decision Trees to Ensemble Models

Semi-Supervised Machine Learning: Leveraging Unlabeled Data

Evaluating Binary Classifiers: Performance Metrics Explained

Machine Learning Evaluation Metrics: Assess AI Model Performance

Digit Recognition with Supervised Machine Learning

Logistic Regression and Feature Selection for Binary Classification