Machine learning (ML) is a subfield of artificial intelligence (AI) and an essential part of data science. It employs statistical methods to classify or predict patterns in data which can help gather insights for business intelligence, customer experience, market research and other drivers of decision-making.
ML can be supervised, unsupervised and reinforced.
Machine learning is used in data mining projects for topic, feature and aspect classification, text parsing, semantic clustering and other tasks. These are essential in AI techniques such as named entity recognition (NER), natural language processing (NLP), sentiment analysis, semantic search and others. They are all are critical to extracting insights from big data.
Machine learning models are self-learning because of artificial neural networks (ANNs) that are encoded in them. ANNs are algorithms that understand data points and correlate patterns as humans do, making ML models more intelligent as they process more and more data.
The more neural layers ANNs have, the greater their capacity to semantically understand data across millions of entities represented in the form of Knowledge Graphs. This advanced form of ANN algorithms translates to Deep Learning (DL)—a subfield that can recognize highly complex patterns in any kind of data for analytical and predictive modelling.
ML models need to be trained to provide insights from big data. When trained with quality data, they can be used successfully for social media sentiment analysis and comment analysis to extract brand, customer and market insights.
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