In the new global economy, machine learning has become a central issue for most research fields because it offers techniques to solve an extremely demanding real-world problem. The study of machine learning is important for answering fundamental questions related to science and engineering. There are three subfields of machine learning: Supervised learning in which training will only take place if the data has been labeled and consists of desired inputs and outputs, Unsupervised learning where the training data did not need to be labeled and the environment only produced inputs without specific objectives and finally reinforcement learning which the characteristics of the information available in the training data falls between supervised and unsupervised and this type of learning occurs based on feedback received through interactions with external environments. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an Original Essay Both supervised and unsupervised learning are suitable for data analysis, while reinforcement learning is a solution for handling problems related to the decision-making process. With the rapid emergence of machine learning trends, the need to improve traditional machine learning to modern machine learning is very great. Improvement in terms of software (algorithm) and hardware is the key to achieving advanced machine learning that can handle today's machine learning. Several advanced learning methods have been mentioned to improve traditional machine learning, including: Representational learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Parallel learning is essentially based on the parallel computing environment. Parallel computing defined as a set of interconnection processes between processing elements and memory modules. In machine learning, parallel computing has improved traditional machine learning by implementing the use of a multi-core processor instead of a single processor[2]. Some researchers have discussed and applied parallel computing in order to process emitted machine learning. Qiu and. al. is accompanied by a summary paper explaining how big data was processed using machine learning. As data becomes large and complex, traditional machine learning faces challenges in training this data. Therefore, six advanced learning methods were introduced as previously discussed. Next, five issues on machine learning in big data were discussed. One of the challenges includes understanding large-scale data. In order to solve this problem, a distributed framework based on parallel computing is suggested. The alternating direction multiplier method (ADMM), the framework that can produce an algorithm with diffusion and scaling capability, is very suitable. ADMM is able to divide multiple problems and helps identify solutions by coordinating those solutions into smaller problem groups. Then, the use of parallel programming methods was also mentioned to solve the problem of large-scale emitted datasets. After that, Memeti et. al. review of two parallel computing systems planning techniques, namely machine learning and. 231–234, 2017.