Natural Language Processing (NLP) is the field of AI that focuses on the development of algorithms and models that enable computers to understand and generate human language. In this course, we will explore the fundamentals of NLP, including language modeling, sentiment analysis, and advanced techniques such as transformer networks.
Language Modeling: Language modeling is the task of predicting the next word in a sentence based on the context of the previous words. Language models are used in a wide range of NLP tasks such as speech recognition, machine translation, and text generation.
Sentiment Analysis: Sentiment analysis is the task of determining the sentiment or emotion expressed in a piece of text. It is used to analyze customer feedback, reviews, and social media posts to understand people’s opinions about a product, service or topic. Sentiment analysis algorithms use techniques such as lexicon-based methods, rule-based methods, and machine learning methods to classify text into positive, negative, or neutral categories.
Advanced Techniques: Recent years have seen the development of advanced NLP techniques such as transformer networks. These architectures are based on the transformer architecture, which uses self-attention mechanisms to allow the model to weigh the importance of different words in a sentence. These architectures are pre-trained on large amounts of data and fine-tuned on specific tasks such as language translation, text summarization, and question answering.
Applications of NLP: NLP techniques are widely used in various domains such as:
- Chatbots and virtual assistants: understanding and generating human language for natural conversation
- Text summarization: automatically generating a short summary of a long text
- Machine translation: automatically translating text from one language to another
- Social media analysis: understanding and analyzing opinions and emotions expressed in social media posts
- Email filtering: automatically categorizing emails as spam or non-spam
In this course, students will learn the fundamentals of NLP, including language modeling, sentiment analysis, and advanced techniques such as transformer networks. They will also explore various applications of NLP in different fields and have the opportunity to work on a hands-on project to apply their knowledge and skills to a real-world problem.