Machine Learning Analysis And Review Of Machine Learning Challenges In Big Data

Machine learning is the knowledge that helps computers do new things without specific programming and by modelling their own behaviour. We said that machine learning as a subset of artificial intelligence is divided into three general categories with supervision, unsupervised and reinforcement. Machine learning is present in different parts of people's lives and different services are created with the help of this knowledge. Machine learning (ML) continues to demonstrate its power in a wide variety of applications. This issue has received more attention in recent years due to the emergence of big data. The ML algorithm was never at its best until it was challenged by big data. Big data enabled the ML algorithm to discover more precise patterns and make more timely and accurate predictions than before. On the other hand, it raised big challenges in ML such as model scalability and distributed computing. In this paper, a framework of ML in Big Data (MLBID) will be introduced to guide the discussion to its opportunities and challenges. Machine learning, as the name suggests, involves learning systems from existing data using algorithms that are Iteratively learn from datasets and analyse the data to develop or train models. This allows systems to discover hidden ideas without having to explicitly plan where to look for them.

کلیدواژه ها:

data ، artificial intelligence ، machine learning ، algorithm