Python NLP : Unleashing the Power of Natural Language Processing for Data Insight and Innovation

Overview

🕮 Off the shelf (OTS)

This course is designed for data scientists, machine learning engineers, developers, and analysts who wish to gain practical experience in applying Natural Language Processing (NLP) techniques using Python. It is especially suited to professionals working with unstructured text data or developing language-based machine learning models.

Familiarity with Python programming is required. Some knowledge of statistics, linear algebra, and machine learning concepts will be helpful but is not essential.

The Python Natural Language Processing (NLP) Training Course introduces participants to the core techniques and tools used in modern NLP. Through hands-on exercises and real-world datasets, attendees will explore text processing pipelines, tokenisation, vectorisation, and embedding methods. The course covers a range of traditional and deep learning-based approaches to text classification, sentiment analysis, topic modelling, and named entity recognition. Emphasis is placed on practical implementation using widely adopted Python libraries such as NLTK, spaCy, Scikit-learn, and Hugging Face Transformers.

Key Topics Covered:
• Fundamentals of text processing: tokenisation, stopwords, stemming, lemmatisation
• Feature extraction from text using Bag-of-Words, TF-IDF, and word embeddings
• Text classification techniques for sentiment analysis and spam detection
• Topic modelling using Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF)
• Named Entity Recognition and Part-of-Speech tagging with spaCy
• Introduction to transformer-based models (e.g., BERT) for advanced NLP tasks

The course is delivered over two days and includes hands-on exercises to reinforce learning.