Overview of NLP

The need for Natural Language Processing (NLP) arises from the fact that human language is a complex and nuanced form of communication that machines struggle to understand and process. NLP addresses this gap by enabling computers to comprehend, analyze, and generate human language.

Think about when you type a question into a search engine like Google. NLP helps Google figure out what you're looking for, even if you didn't use the exact words. It's like having a smart assistant that can understand and help you with all sorts of language-related tasks, from translating languages to reading and summarizing long articles, and even having conversations with you.

In essence, NLP is like giving superpowers to computers so they can understand, analyze, and even talk like humans. It's like teaching your computer to understand what you're saying, even if you use slang, different words, or even mistakes. Now, let's look at different techniques that makes up NLP.

NLP Pipeline

NLP pipeline is like a series of steps that a computer follows to process and understand human language. Just like a pipeline in any industry, each step in the NLP pipeline contributes to refining and enhancing the final output, making it a powerful tool for extracting valuable information and insights from textual data.

The idea behind NLP is to take an unstructured data (text) and convert it to something with a structure and make a sense out of it. Let's look at the techniques we need ti achieve this.

Application of NLP

NLP is indispensable in today's data-driven world due to its ability to harness the vast amount of textual data that humans generate daily. The need for NLP arises from several applications, such as:

• Information retrieval
• Sentiment analysis
• Language translation
• Chatbots
• Text summarization
• Enhanced search engines
• Healthcare insights
• Legal and compliance
• Social media analysis
• Personalized content

➡️ Next, we'll look at NLP tools and libraries... 🎯.