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What you'll learn

  • Foundations of NLP: Gain a deep understanding of the foundational concepts and techniques in natural language processing, including text preprocessing, tokenization, and text normalization.
  • Statistical and Machine Learning Methods: Learn how to apply statistical and machine learning techniques to solve NLP tasks such as text classification, sentiment analysis, and named entity recognition.
  • Deep Learning for NLP: Explore advanced deep learning models for NLP, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers, and learn how to apply them to tasks such as sequence labeling and machine translation.
  • Semantic Analysis: Understand how to extract semantic meaning from text using word embeddings, semantic similarity, and topic modeling techniques.
  • Syntax and Parsing: Learn about syntactic analysis and parsing techniques, including part-of-speech tagging, dependency parsing, and constituency parsing.
  • Practical Applications: Apply your knowledge to real-world NLP projects across various domains, including sentiment analysis, chatbots, question-answering systems, and more.

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