Mar 31, 2025 · Azure Machine Learning has a large library of algorithms from the classification, recommender systems, clustering, anomaly detection, regression, and text analytics families. Each is designed to address a different type of machine learning problem. For more information, see How to select algorithms.

Apr 22, 2025 · This cheatsheet will cover most common machine learning algorithms. For example, they can recognize images, make predictions for the future using the historical data or group similar items together while continuously learning and improving over time.

Apr 13, 2022 · In this cheat sheet, you'll have a guide around the top supervised machine learning algorithms, their advantages and disadvantages, and use-cases.

Jan 24, 2025 · Explore this comprehensive machine learning cheat sheet covering algorithms, metrics, libraries and concepts. Ideal for interviews and practical ML applications

Jul 16, 2019 · Think of this as the one-stop-shop/dictionary/directory for your machine learning algorithms. The algorithms have been sorted into 9 groups: Anomaly Detection, Association Rule Learning, Classification, Clustering, Dimensional Reduction, Ensemble, Neural Networks, Regression, Regularization.

Scikit-learn provides algorithms like linear regression, logistic regression, decision tree models, random forest regression, gradient boosting regression, gradient boosting classification, K-nearest neighbors, Support Vector Machine, Naive Bayes, neural networks, and a lot more.

Jan 24, 2022 · For non-numerical prediction algorithms, you can choose the Classification methods over regression. Supervised Learning: Classification Alike to the regression methods, you need to choose if you would rather speed or accuracy for your outcomes.

Dec 9, 2024 · This extended cheat sheet digs deeper into the world of ML algorithms. From classic methods to cutting-edge techniques, knowing when to apply each algorithm is crucial in solving real-world problems. Keep this handy as a quick reference for your next project!

Oct 19, 2020 · Scikit-learn is the most popular Python library for performing classification, regression, and clustering algorithms. It is an essential part of other Python data science libraries like matplotlib, NumPy (for graphs and visualization), and SciPy (for mathematics).

Scikit-learn is an open-source Python library for all kinds of predictive data analysis. You can perform classification, regression, clustering, dimensionality reduction, model tuning, and data preprocessing tasks.