Natural Language Processing
Natural Language Processing (NLP) is a critical aspect of AI and ML, enabling machines to understand, interpret, and generate human language. In this session, students explore the techniques and algorithms used to process and analyze textual data. They gain insights into tasks such as sentiment analysis, text classification, named entity recognition, and language generation.
Text Preprocessing and Tokenization
This subheading covers the initial steps in NLP, where students learn how to preprocess and tokenize textual data. They explore techniques for cleaning and normalizing text, such as removing punctuation, lowercasing, and handling special characters. Students also dive into tokenization, where they split text into individual words or subword units, enabling further analysis and modeling.
Sentiment Analysis
Sentiment analysis is a popular NLP task that aims to determine the sentiment or emotion expressed in a piece of text. In this subheading, students explore different approaches to sentiment analysis, ranging from rule-based methods to machine learning-based techniques. They learn about feature extraction, sentiment lexicons, and sentiment classification models. Students gain hands-on experience in building sentiment analysis models and applying them to analyze sentiment in social media posts or customer reviews.
