Machine learning

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.

• Supervised learning: Algorithms are trained with industry-specific data to gain insights. This method is most frequently used for business applications.
• Unsupervised learning: Algorithms analyze millions of data points and begin recognizing patterns on their own. It is commonly used in areas such as weather data clustering.
• Reinforcement learning: Advanced ML where algorithms learn to perceive and interpret their environment and take corrective actions through trial and error. Think: AI-powered robotics.

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.

Brought to you by

Try Vista Social for Free

A social media management platform that actually helps you grow with easy-to-use content planning, scheduling, engagement and analytics tools.

Get Started Now

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.