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Natural Language Processing (NLP)

Natural Language Processing (NLP)

This course provides a comprehensive introduction to Natural Language Processing, covering fundamental concepts, popular techniques, and real-world applications. Learners will gain the skills to process, analyze, and understand human language using computational methods.

$35

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Has discount
Expiry period 6 Months
Made in English
Last updated at Sat Sep 2025
Level
Beginner
Total lectures 7
Total quizzes 6
Total duration 01:19:54 Hours
Total enrolment 0
Number of reviews 0
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Short description This course provides a comprehensive introduction to Natural Language Processing, covering fundamental concepts, popular techniques, and real-world applications. Learners will gain the skills to process, analyze, and understand human language using computational methods.
Outcomes
  • Explain the core challenges of NLP, such as ambiguity, and identify key applications like sentiment analysis and machine translation.
  • Perform standard text preprocessing tasks, including tokenization, stemming, lemmatization, and stop-word removal.
  • Implement and understand traditional NLP techniques like n-gram models and TF-IDF for text representation.
  • Apply classic machine learning algorithms, such as Naive Bayes and SVMs, to solve NLP problems like text classification.
  • Describe the architecture and significance of modern deep learning models for NLP, including RNNs, LSTMs, and the Transformer.
  • Critically evaluate the ethical implications of NLP, including model bias and the potential for misuse, and discuss future trends in the field.
Requirements
  • Technical: A computer capable of running modern data science software.
  • Software: Python and libraries such as NLTK, Scikit-learn, and later, a deep learning framework like TensorFlow or PyTorch.
  • Prerequisites: Solid Python programming skills and a foundational understanding of Machine Learning concepts (as covered in the

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