Course Details

Text Classification and Categorization: Used in spam detection, sentiment analysis, and topic labeling.

Sentiment Analysis: Analyzing the sentiment expressed in a piece of text, such as determining whether a review is positive or negative.

Machine Translation: Automatically translating text from one language to another, like Google Translate.

Chatbots and Virtual Assistants: Providing automated responses in customer service or personal assistants like Siri and Alexa.

Named Entity Recognition (NER): Identifying and classifying entities in text into categories like names, dates, and organizations.

Text Summarization: Creating a concise summary of a larger body of text, useful for news aggregation and research.

Speech Recognition: Converting spoken language into text, which is fundamental in voice-controlled systems.

Language Generation: Creating human-like text, as seen in creative writing aids and automated news articles.

Information Retrieval and Search Engines: Enhancing the ability to retrieve relevant information based on user queries.

Optical Character Recognition (OCR): Converting different types of documents, such as scanned paper documents or PDFs

Question Answering Systems: Providing accurate answers to questions posed in natural language, like IBM Watson.

Document Analysis: Automating the understanding and extraction of information from documents, such as contracts or invoices.