Machine learning techniques have prompted at the forefront over the last few years due to the advent of big data. Machine learning is a precise subfield of artificial intelligence (AI) that seeks to analyze the massive data chunks and facilitate the system to learn the data automatically without the explicit support of programming. The machine learning algorithms attempt to reveal the fine-grained patterns from the unprecedented data under multiple perspectives and to build an accurate prediction model as never before. For the purpose of learning, the machine learning algorithm is sub-categorized into four broad groups include supervised learning, semi-supervised learning, unsupervised learning, and reinforcement learning. Whenever the new unseen data is fed as input to the machine learning algorithm, it automatically learns and predicts the forthcoming by exploiting the previous experience over time. Machine learning is continually liberating its potency in a broad range of applications, including the Internet of Things (IoT), computer vision, natural language processing, speech processing, online recommendation system, cyber security, neuroscience, prediction analytics, fraud detection, and so on.
