Best of Scikit — 2025

1. 1
   Article

   Machine Learning Mastery·1y

   10 Python One-Liners for Machine Learning Modeling

   Python's capability for concise one-liners can streamline the creation and evaluation of machine learning models. This guide covers ten useful one-liners, including loading data with Pandas, removing missing values, encoding categorical data, dataset splitting, model initialization and training, and cross-validation. These compact codes simplify processes such as feature scaling and pipeline building, essential for effective model development and deployment.

   88

   3
2. 2
   Article

   Machine Learning Mastery·51w

   10 Python One-Liners That Will Simplify Feature Engineering

   Ten practical Python one-liners for feature engineering tasks including standardization, min-max scaling, polynomial features, one-hot encoding, discretization, logarithmic transformation, ratio creation, low variance removal, multiplicative interactions, and outlier tracking. Each technique uses popular libraries like scikit-learn and pandas to transform raw data into meaningful features for machine learning models.

   82
3. 3
   Article

   Machine Learning Mastery·1y

   Next-Level Data Science (7-Day Mini-Course)

   Understand the evolution of data science from statistical analysis to the use of machine learning models. This 7-day mini-course is designed for developers with basic Python and machine learning knowledge, focusing on practical applications and explaining data through machine learning models. Participants will learn how to handle data, perform linear regression, interpret factors, select features, and utilize decision trees and random forests to build accurate models.

   36
4. 4
   Article

   AI·1y

   ML Algorithms From Scratch

   Comprehensive implementations of machine learning algorithms from scratch using NumPy and with popular libraries like scikit-learn. Detailed explanations cover mathematical concepts and practical examples to help understand the inner workings of these algorithms.

   35
5. 5
   Article

   Dev Squad·1y

   ML Algorithms From Scratch

   A thorough collection of machine learning algorithms, each implemented from scratch using NumPy and with popular libraries like scikit-learn. The repository includes detailed explanations, mathematical concepts, and practical examples to aid comprehension.

   33
6. 6
   Article

   freeCodeCamp·51w

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

   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.

   28
7. 7
   Article

   Machine Learning Mastery·1y

   10 Python One-Liners for Feature Selection Like a Pro

   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.

   22
8. 8
   Article

   DuckDB·1y

   Machine Learning Prototyping with DuckDB and scikit-learn

   This post explores how DuckDB, an efficient data management system, complements scikit-learn, a popular machine learning library, in developing a species prediction model using the Palmer Penguins dataset. Key steps include data preprocessing with DuckDB, model training using a Random Forest classifier, and three inference methods to achieve predictions: using Pandas, DuckDB UDF row by row, and DuckDB batch style. Performance implications of UDFs are discussed, highlighting their utility despite slower execution times compared to Pandas.

   10
9. 9
   Article

   Towards AI·1y

   Scikit-learn from A to Z: The Complete Guide to Mastering Machine Learning in Python

   Scikit-learn is an open-source machine learning library built on Python, designed to handle various ML tasks such as supervised learning, unsupervised learning, model evaluation, and data preprocessing. This guide explores its features, offering insights into how it can be used from basic model creation to efficient deployment.

   10