Categorical variables must be encoded before use in scikit-learn models. This article covers 3 of the core strategies and best practices for handling categorical features in machine learning with code examples.
This article provides an overview of the main feature selection techniques available in scikit-learn.
This article provides an overview of how to evaluate classification model performance in scikit-learn using metrics like accuracy, precision, recall, F1 score, and ROC AUC. It includes code examples and explanations of each metric.
This crash course is designed to provide you with a solid foundation in Scikit-learn to start building machine learning models in Python. It introduces key concepts like model evaluation and selection, discuss the major algorithms like regression and classification, and walk through the typical Scikit-learn workflow for developing predictive models.
This article provides an overview of techniques like oversampling, undersampling, and adjusting class weights that can be used in scikit-learn to handle imbalanced data and improve model metrics. It also covers best practices like stratification and SMOTE oversampling.
This tutorial demonstrates key unsupervised learning techniques in scikit-learn through code examples, covering dimensionality reduction, clustering algorithms, association rule learning, and anomaly detection. A practical guide to leveraging unsupervised learning to derive insights from unlabeled datasets.
Ensemble learning combines multiple machine learning models to improve overall predictive performance. This article provides an introduction to ensemble techniques like bagging, boosting, voting, and stacking available in scikit-learn.
This article delves into the realm of Explainable AI (XAI), illuminating the current techniques and methodologies employed to interpret black box models. The importance of XAI, examples of practical implementation, and future potential are all explored in an accessible and engaging manner.
Explore the evolving role of Large Language Models (LLMs) in automating data science tasks, making the field more accessible. Understand their potential, limitations, and the continued importance of human expertise.
This in-depth, 3-part Pandas crash course covers everything from the core DataFrame and Series data structures to essential data manipulation operations to advanced analysis and visualization tools. Through concrete examples and code snippets, you’ll learn foundational Pandas to start wielding this versatile data analysis library for tasks.
This tutorial offers a deep-dive into SQL for data scientists, starting with fundamental concepts before progressing to advanced techniques and optimization strategies. Each section is fortified with examples and exercises to reinforce learning, with the ultimate goal of transforming readers into proficient SQL users.
This article provides an in-depth look at cross-validation in scikit-learn, a critical procedure in machine learning that ensures the robustness of a model against unseen data. Using Python-based examples, we explore the methods of performing cross-validation, its benefits, and how to interpret the results.
