AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Ücretsiz AI kütüphanesi
84 Herkesin modern yapay zekayı anlayabilmesi için bağımsız bir 501(c)(3) kar amacı gütmeyen kuruluşu tarafından oluşturulmuş sade İngilizce kılavuzlar, yapılandırılmış öğrenme yolları ve açık bir kütüphane.
Buradan başlayın
Each course includes explicit outcomes, mapped competencies, practice activities, and an applied capstone.
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
Konu parçaları
İlgilendiğiniz alana atlayın. Her parçanın birden fazla sade İngilizce kılavuzu vardır.
Tam kütüphane
84 arasında 1019 gösterilen kılavuzlar. Parçaya göre filtreleyin veya yukarıda arayın.
Artificial intelligence (AI) is the field of building computer systems that perform tasks such as recognizing patterns, understanding language, planning…
TemellerMachine-learning systems learn by adjusting a model using data and a training objective.
TemellerA neural network is a machine-learning model made of connected mathematical operations with adjustable parameters.
TemellerDeep learning is a branch of machine learning that uses neural networks with multiple layers to learn representations of data.
TemellerAI training is the process of adjusting a machine-learning model using examples and a learning objective.
TemellerInference is using a trained model to produce an output from a new input.
TemellerData is the recorded information a machine-learning system learns from or processes.
TemellerA machine-learning model is a mathematical system that maps inputs to outputs using a structure and learned parameters.
TemellerMachine learning builds models whose behavior is fitted from examples rather than written entirely as explicit rules.
TemellerSupervised learning fits a model using examples that pair inputs with target outputs.
TemellerUnsupervised learning looks for structure in data without a target label for every example.
TemellerGenerative AI produces outputs such as text, images, audio, or code using learned statistical patterns and supplied context.
Check what you learned with topic quizzes, then explore our structured courses and current certification requirements. Core guides remain free to read.