Machine learning

What do you mean by Machine Learning?

Machine learning (ML) is a fundamental component of artificial intelligence (AI) and plays a crucial role in data science. By utilizing statistical techniques, ML enables the classification and prediction of patterns within data, leading to valuable insights for various purposes such as business intelligence, customer experience enhancement, market research, and decision-making.

ML can be categorized into three main types: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning involves training algorithms with industry-specific data to derive insights, making it the most commonly used method for business applications.

Unsupervised learning, on the other hand, allows algorithms to independently analyze vast amounts of data points and recognize patterns without explicit guidance. It finds utility in areas like weather data clustering.

Reinforcement learning represents an advanced form of ML where algorithms learn to perceive and interpret their environment. Through trial and error, they take corrective actions. This type of learning is often associated with AI-powered robotics.

ML finds extensive application in data mining projects, performing tasks such as topic, feature, and aspect classification, text parsing, semantic clustering, and more. These techniques are essential in AI fields like named entity recognition (NER), natural language processing (NLP), sentiment analysis, and semantic search. They enable the extraction of insights from large-scale datasets.

Machine learning models possess self-learning capabilities due to the incorporation of artificial neural networks (ANNs). ANNs are algorithms that comprehend data points and establish correlations, mimicking human cognitive processes. As ML models process more data, they become increasingly intelligent.

Deep learning (DL) is a subfield of ML that relies on ANNs with multiple neural layers. This enables DL to semantically understand data across millions of entities, represented in the form of Knowledge Graphs. Consequently, DL can recognize highly complex patterns in any type of data for analytical and predictive modeling.

To extract insights from big data, ML models need to undergo training using high-quality data. Once trained, they can be effectively utilized for tasks such as social media sentiment analysis and comment analysis, facilitating the extraction of brand, customer, and market insights.