At bridge_ci, we use a variety of machine-learning techniques to model different types of events, trends, strategies, and phenomena, which are explained below.
Supervised Learning Involves training a model using labeled data, meaning that the desired output (labels) is provided alongside the input data. Once trained, the model can accurately predict new, unseen data outcomes. Examples include classification and regression problems.
Structured Data: Labeled data typically comes in the form of tabular data, text documents, or images with predefined classes or targets assigned during collection or processing.
Natural Language Processing (NLP): Supervised learning in NLP involves training models to perform tasks such as sentiment analysis, named entity recognition, or topic modeling, where the ground truth labels help the model learn the appropriate mappings between inputs and outputs.
Unsupervised Learning: Utilizes unlabeled data to discover inherent structures or patterns within the data itself. No explicit labels are provided, and the aim is to group similar items or extract relevant features. Examples include clustering and dimensionality reduction.
Structured Data: Unstructured data sources like social media posts, emails, or sensor readings can be processed using unsupervised learning techniques to reveal insights or trends.
Natural Language Processing (NLP): Unsupervised learning in NLP might involve topic discovery in large collections of texts, document clustering, or word embedding generation, where the model discovers relationships between words or phrases without prior knowledge of their meanings.
Semi-Supervised Learning: Combines labeled and unlabeled data to enhance the learning process. Models trained using semi-supervised approaches benefit from the larger volume of unlabeled data, which helps refine the representations learned from the smaller number of labeled examples.
Structured Data: Similar to supervised learning, semi-supervised learning can utilize labeled data along with unlabeled data to improve model performance. However, since unlabeled data does not come with labels, the model must rely on auxiliary information or assumptions to leverage the unlabeled data effectively.
Natural Language Processing (NLP): Semi-supervised learning in NLP might involve utilizing a small amount of labeled data combined with a much larger corpus of unlabeled data to improve the quality of language models, reduce overfitting, or enable zero-shot learning capabilities.