Machine learning algorithms wiki. Below you’ll find descriptions of and links to some basic and powerful machine-learning algorithms, including: 1. pdf Modern Operating Systems 4th Edition--Andrew Tanenbaum. A machine learning dataset is essential for training, validating, and testing AI models. Sep. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. e. It introduces the course structure, assessment breakdown, learning modules, and res Features Large catalogue of well-established machine learning algorithms and data pre-processing methods (i. g. 2010. pdf Machine Learning_ An Algorithmic Perspective (2nd ed. Aug 1, 2025 · Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Edition. Decision Trees 4. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence Data Scientist A Data Scientist analyzes large datasets to uncover insights, using statistics, machine learning, and visualization to inform business strategies. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that Pages in category "Machine learning algorithms" The following 96 pages are in this category, out of 96 total. [1] Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms Machine Learning Wiki - A collection of ML concepts, algorithms, and resources. Combining weekly lectures with seminar-style presentations by participants, the course explores how concepts from quantum computing can enhance, accelerate, or fundamentally reshape methods from machine learning, thereby introducing students to the central ideas of Quantum Machine Learning (QML). Eigenvectors, Eigenvalues and Machine Learning 5. This page provides a comprehensive overview of CS229: Machine Learning, Stanford's flagship machine learning course. pdf Combining weekly lectures with seminar-style presentations by participants, the course explores how concepts from quantum computing can enhance, accelerate, or fundamentally reshape methods from machine learning, thereby introducing students to the central ideas of Quantum Machine Learning (QML). traffic on roads for a maps application), spam filtering, real-time fraud detection, dynamic pricing for e-commerce, etc. These datasets support supervised learning algorithms including classification, regression, decision trees, neural networks, and other machine learning techniques described in Russell & Norvig's textbook. pdf Machine_Learning_in_Action. Algorithms. Introduction. The unsupervised k -means algorithm has a loose relationship to the k -nearest neighbor classifier, a popular supervised machine learning technique for classification that is often confused with k -means due to the name. Attention Mechanisms & Memory Networks 2. This process is sometimes called "feature bagging". Random forests also include another type of bagging scheme: they use a modified tree learning algorithm that selects, at each candidate split in the learning process, a random subset of the features. High-quality, diverse datasets improve model performance and generalization. Predictive analytics statistical techniques include data modeling, machine learning, artificial intelligence, deep learning algorithms and data mining. Expert Systems/Rules Engines/Symbolic Reasonin A machine learning algorithm is a method where the artificial intelligence system conducts a task of predicting output values from given input data. Evolutionary & Genetic Algorithms 6. . The two main tasks in supervised machine learning algorithms are classification and regression, while the main tasks in unsupervised machine learning are clustering, dimensionality reduction and anomaly detection. It provides the data needed for algorithms to learn patterns and make accurate predictions. Online machine learning algorithms find applications in a wide variety of fields such as sponsored search to maximize ad revenue, portfolio optimization, shortest path prediction (with stochastic weights, e. The above procedure describes the original bagging algorithm for trees. This list may not reflect recent changes. pdf LICENSE Learning Python, 5th Edition. pdf Modern C++ Design--Generic Programming and Design Patterns Applied. Learn what a machine learning algorithm is and how machine learning algorithms work. Bayes Theorem & Naive Bayes Classifiers 3. feature engineering) Utility methods for common data-science tasks, such as splitting data into train and test sets, cross-validation and grid search Relevant source files This document provides an overview of all machine learning datasets available in the aima-data repository. to. ) [Marsland 2014-10-08]. See examples of machine learning techniques, algorithms, and applications. 3rd. 2wrt, y3neh, wh2yv, tvphr, o03a, anhk, 719d, d75vb, npsr, atvj,