The basic syllabus for NLP includes the following:
- Natural Language Processing: An Overview: Learn the principles of text processing, such as stemming and lemmatization. Investigate sentiment analysis methods based on machine learning. Create a model for speech tagging.
- Natural Language Processing: Computing: Advanced techniques like word embeddings, deep learning attention, and more will be covered. Using recurrent neural network topologies, create a machine translation model.
- Natural Language Communication: Learn how to convert speech to text and vice versa using voice user interface approaches. Create a deep neural network-based voice recognition model.
All of these helps an individual to:
- Learn how to process speech and analyze text using cutting-edge natural language processing techniques.
- Create probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to educate computers to perform tasks like speech recognition and machine translation.
As such, even if a course outline is defined, an individual can take initiative to achieve greater heights in an organization.