Natural Language Processing(NLP) -AI Module

Natural Language Processing(NLP) -AI Module


3x per week

45 sessions

session duration


Typical class size




Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. It encompasses the ability of computers to understand, interpret, and generate human language in a manner that is both meaningful and contextually relevant. NLP techniques enable machines to comprehend and process natural language text, speech, and even gestures, allowing for applications such as language translation, sentiment analysis, chatbots, text summarization, and more. NLP has significant implications for various industries, including healthcare, finance, customer service, and marketing, where it can automate tasks, improve communication, and extract valuable insights from vast amounts of textual data.

What you'll learn

     1. Introduction to NLP:

    •    - Overview of NLP and its applications.
    •    - Understanding the challenges and opportunities in NLP.
    •    - Introduction to key concepts like tokenization, stemming, lemmatization, and part-of-speech tagging.

     2. Text Processing and Preprocessing:

    •    - Text cleaning techniques: removing noise, punctuation, and stop words.
    •    - Tokenization and normalization.
    •    - Stemming and lemmatization.
    •    - Handling text encoding issues.

     3. Text Representation:

    •    - Bag-of-Words (BoW) model.
    •    - TF-IDF (Term Frequency-Inverse Document Frequency).
    •    - Word embeddings: Word2Vec, GloVe, FastText.
    •    - Contextual embeddings: BERT, GPT, XLNet.

     4. Sentiment Analysis:

    •    - Understanding sentiment analysis tasks.
    •    - Text classification approaches for sentiment analysis.
    •    - Building sentiment analysis models using machine learning and deep learning techniques.

     5. Named Entity Recognition (NER):

    •    - Introduction to NER and its applications.
    •    - Different approaches for NER: rule-based, machine learning, deep learning.
    •    - Building NER models using popular libraries like SpaCy.

     6. Text Classification:

    •    - Text classification tasks: topic classification, spam detection, sentiment analysis.
    •    - Building text classification models using Naive Bayes, SVM, and deep learning techniques like CNNs and LSTMs.

     7. Language Modeling:

    •    - Introduction to language modeling.
    •    - N-gram language models.
    •    - Building language models with recurrent neural networks (RNNs) and transformers.

     8. Sequence-to-Sequence Models:

    •    - Introduction to sequence-to-sequence (seq2seq) models.
    •    - Applications like machine translation, text summarization, and chatbots.
    •    - Building seq2seq models using LSTM, GRU, and attention mechanisms.

     9. Topic Modeling:

    •    - Introduction to topic modeling techniques like Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF).
    •    - Building topic models to extract latent topics from text data.

     10. Text Generation and Dialog Systems:

    •    - Introduction to text generation tasks.
    •    - Building generative models for text generation using RNNs, GANs, and transformer-based architectures.
    •    - Introduction to dialog systems and chatbots.

     11. Advanced Topics:

    •    - Advanced techniques in NLP like transfer learning, domain adaptation, and multi-task learning.
    •    - Ethical considerations and bias in NLP.
    •    - Current research trends and future directions in NLP.

     12. Hands-on Projects:

    •    - Implementing NLP techniques in real-world projects.
    •    - Working on sentiment analysis, named entity recognition, text classification, and other NLP tasks.
    •    - Experimenting with different datasets and models to gain practical experience.



Course Curriculum

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  • Level
    Basic to Advanced
  • Lectures
    45 Lectures
  • Duration
  • Language
  • Access
  • Certificate

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