Natural Language Processing

Whether you’re a novice or an experienced professional, our curated Machine Learning courses cater to diverse skill levels, providing a comprehensive and hands-on learning experience.

Skills You Will Gain

Data Cleaning








This course includes

Syllabus Overview

  • – What is NLP?
    – Applications of NLP
    – Tokenization, Stopword Removal, Stemming, and Lemmatization
  • – Bag of Words (BoW), TF-IDF
    – Word Embeddings (Word2Vec, GloVe)
    – N-grams, Markov Models, Hidden Markov Models (HMMs)
  • – Role of POS tagging in NLP
    – POS tagging algorithms
    – Introduction to NER
    – Rule-based and machine learning-based NER systems
  • – Understanding sentiment analysis
    – Sentiment analysis techniques
    – Supervised text classification
    – Building text classifiers using different algorithms
  • – Introduction to sequence-to-sequence models
    – Applications of Seq2Seq models
    – Attention mechanisms in NLP applications
  • – Deep dive into the Transformer architecture
    – Pre-trained Transformers (e.g., BERT, GPT)
    – Fine-tuning pre-trained Transformers for NLP tasks

  • – Introduction to text generation
    – Recurrent Neural Networks (RNNs) for text generation
    – Generating text with RNNs and LSTM
    – In-depth exploration of GPT-3
  • – Advanced text classification techniques
    – Building custom NER models
    – Evaluation metrics for NER

– Introduction to Automatic Speech Recognition (ASR)
– Text-to-Speech (TTS) synthesis
– Integrating ASR and TTS into NLP applications

– Ethical considerations in NLP (bias, privacy)
– Recent developments and future trends in NLP
– Student presentations on NLP projects or research

Transform Your Skills: Enroll Now to Learn AI

This condensed roadmap still covers essential NLP concepts and techniques while being more organized in a 10-week format. You can adjust the pace and depth of each topic based on the needs of your course and students.

Scroll to Top