Machine learning is a branch of artificial intelligence that focuses on developing algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data. The goal of machine learning is to create systems that can automatically improve their performance over time without being explicitly programmed. This is achieved through the use of algorithms that analyze and interpret patterns in data, allowing the system to make informed decisions or predictions.

There are several types of machine learning approaches, including supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided, allowing the system to learn the relationship between inputs and outputs. Unsupervised learning involves training the algorithm on unlabeled data, where the system must identify patterns and relationships on its own. Reinforcement learning involves training the algorithm through a system of rewards and punishments, where the system learns to make decisions based on maximizing rewards.

Machine learning has a wide range of applications across various industries, including healthcare, finance, marketing, and more. It is used for tasks such as image and speech recognition, natural language processing, recommendation systems, and predictive analytics. As the field of machine learning continues to advance, it is expected to play an increasingly important role in shaping the future of technology and innovation.
Strojové učení je odvětvím umělé inteligence (AI), které umožňuje počítačům, systémům a programům učit se a zlepšovat se zkušenostmi, aniž by byly explicitně programovány. Zahrnuje použití algoritmů, které dokážou analyzovat data a provádět predikce a rozhodnutí tím, že rozpoznávají vzory v komplexních datových sadách. Cílem strojového učení je vyvíjet algoritmy a modely, které se mohou učit z dat, identifikovat vzory v datech a provádět rozhodnutí a predikce na základě toho, co se naučily. Algoritmy strojového učení mohou být trénovány na různých typech dat, od textu po obrázky a od zvuku po video, a mohou být použity v mnoha různých aplikacích, včetně lékařské diagnostiky, autonomních vozidel, predikce na burze, detekce podvodů a zpracování přirozeného jazyka.