Building Multi-Modal & Agentic AI on Azure: A Step-by-Step Approach

 Natural Language Processing

NLP (Natural Language Processing) is a branch of AI that focuses on enabling machines to understand, interpret, and generate human language. It is used in various applications, including chatbots, language translation, speech recognition, and sentiment analysis.

To understand NLP, it's essential to understand the basic components and techniques involved in processing human language. Here's a simplified tabular breakdown of these components and techniques:

NLP uses various algorithms and techniques to process and analyze human language, such as:

1. Machine Learning Algorithms - used for training models to perform specific NLP tasks, such as sentiment analysis or named entity recognition.

2. Deep Learning Algorithms - used for training deep neural networks that can process and analyze complex natural language data.

3. Rule-Based Systems - used for developing systems that follow a set of predefined rules to analyze and process language data.

There are various algorithms used in NLP such as:

Overall, NLP is an essential tool in today's data-driven world, and it has vast potential for solving real-world problems related to language processing and analysis.