NLP Techniques and Models focuses on the core methods and algorithms used to process and understand human language using computational approaches. It enables the development of systems that can analyze, interpret, and generate text data effectively. This training explains key NLP techniques such as tokenization, stemming, lemmatization, part-of-speech tagging, and named entity recognition. It also covers models including bag-of-words, TF-IDF, word embeddings, and transformer-based architectures. You will learn how these techniques and models are applied in chatbots, sentiment analysis, and machine translation systems. The course also highlights best practices for building accurate and scalable NLP solutions.
Showing the single result
