freeCodeCamp

A comprehensive guide to building multilayer perceptrons (MLPs) for binary classification using three approaches: custom Python implementation, scikit-learn's MLPClassifier, and Keras Sequential models. The tutorial covers fundamental concepts like activation functions, loss functions, and optimization algorithms (SGD vs Adam), then demonstrates practical implementation through a fraud detection project. It includes detailed explanations of forward propagation, backpropagation, and techniques for handling imbalanced datasets using SMOTE, class weights, and regularization methods.

Learn to Build a Multilayer Perceptron with Real-Life Examples and Python Code

Machine Learning Mastery

Feature selection is a critical step in data preprocessing for machine learning tasks. This guide presents ten efficient Python one-liners for selecting meaningful features across different datasets. Methods like variance threshold, correlation-based selection, random forest importance, and PCA are amongst those featured, intended to enhance model performance by focusing on relevant data. The article also covers handling multicollinear features and using techniques such as ANOVA F-Test, mutual information, and L1 regularization for feature selection.

10 Python One-Liners for Feature Selection Like a Pro

A machine learning library for Python built on NumPy, SciPy, and matplotlib, providing simple and efficient tools for data mining and data analysis. Readers can learn about scikit-learn's algorithms, preprocessing techniques, model evaluation metrics, and pipelines for building and deploying machine learning models.

Best of Scikit — May 2025