Natural Language Processing

Description

This Natural Language Processing training course teaches you how to teach machines to understand, interpret, and generate human language for applications like chatbots, translation, sentiment analysis, summarization, and search. You will learn the foundations from tokenization to transformers, work with libraries like spaCy, NLTK, and Hugging Face, and build end-to-end NLP systems that power modern AI applications.

Course Content

Module 1: Introduction to NLP

What is natural language processing
The NLP landscape today
From rule-based to neural NLP

Module 2: Text Preprocessing

Tokenization and normalization
Stemming, lemmatization, stop words
Part-of-speech tagging
Working with spaCy and NLTK

Module 3: Text Representation

Bag-of-words and TF-IDF
Word embeddings: Word2Vec, GloVe
Contextual embeddings with BERT
Sentence and document embeddings

Module 4: Classification and Sentiment

Text classification with machine learning
Sentiment analysis techniques
Topic modeling with LDA
Building a custom classifier

Module 5: Sequence Models

Recurrent neural networks (RNNs)
LSTM and GRU architectures
Sequence-to-sequence models
When sequence models still matter

Module 6: Transformers and LLMs

The transformer architecture
BERT, GPT, and T5 families
Using Hugging Face for NLP
Fine-tuning pre-trained models

Module 7: NLP Applications

Machine translation
Text summarization
Question answering systems
Named entity recognition

Module 8: Deployment and Capstone

Deploying NLP models as APIs
Optimizing for speed and cost
Multilingual NLP considerations
Build a complete NLP project

Duration: 6 – 8 weeks

ENROLL NOW