دانلود کتاب آغازگر 42 الگوریتم یادگیری ماشینی که بیشترین استفاده را دارد

Machine learning refers to the development of AI systems that can perform tasks due to a “learning process” based on data. This is in contrast to approaches and methods in symbolic AI and traditional software development, which are based on embedding explicit rules and logical statements in the code. ML is at the heart of recent advances in statistical AI and the methodology behind technological achievements such as computer programs that outperform humans in tasks ranging from medical diagnosis to complex games. The recent surge of interest in AI is largely due to the achievements made possible by ML. As the term “statistical AI” suggests, ML draws on statistics and probability theory concepts. Many forms of ML go beyond traditional statistical methods, which is why we often think of ML as an exciting new field. However, despite the hype surrounding this technological development, the line between ML and statistics is blurred. There are contexts in which ML is best viewed as a continuum with traditional statistical methods rather than a clearly defined separate field. Regardless of the definitional boundaries, ML is often used for the same analytical tasks that conventional statistical methods have been used for in the past. ML Approaches.
ML is a very active area of research that encompasses a broad and ever-evolving range of methods. Three primary approaches can be distinguished at a high level: supervised learning, unsupervised learning, and reinforcement learning.