Natural Language Processing (NLP)
This course covers Natural Language Processing (NLP), a branch of artificial intelligence focused on enabling computers to understand and process human language. NLP is widely used in applications such as chatbots, sentiment analysis, and machine translation. Students will explore:
- Introduction to NLP: Students will be introduced to the basics of NLP, its real-world applications, and the challenges involved in making machines understand human language.
- Text Preprocessing: The course covers essential text preprocessing techniques, including tokenization, stemming, lemmatization, and removing stop words, to prepare raw text for analysis.
- Feature Extraction: Students will learn methods for representing text data, including bag-of-words, TF-IDF, and word embeddings, which are essential for creating machine-readable data representations.
- Sentiment Analysis and Text Classification: Students will explore popular NLP tasks like sentiment analysis, where they’ll learn to detect emotions in text, and text classification for categorizing documents.
- Language Models and Sequence Modeling: This section covers language models, N-grams, and recurrent neural networks (RNNs), allowing students to understand how models generate text and learn sequential dependencies.
- Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging: Students will learn about recognizing named entities in text and identifying parts of speech, enabling advanced text understanding.
- Hands-On Projects: Through practical projects, students will apply NLP techniques to build applications, such as spam filters, sentiment analyzers, and chatbots, gaining real-world experience in NLP.
This NLP course is ideal for students with a foundation in Python who are interested in AI and machine learning. By the end, students will be able to build basic NLP applications and understand key concepts that power popular language-based tools.