Professional Certificate in Practical NLP: Python Projects for Text Processing

This course is designed for data analysts, software developers, and researchers seeking to apply natural language processing (NLP) techniques to real-world text data problems using Python. Participants will gain hands-on experience in text preprocessing, sentiment analysis, topic modeling, and text generation, equipping them with practical skills to tackle NLP challenges in their projects.

By the end of the course, learners will have completed several Python-based NLP projects, including building a text classification model, analyzing customer reviews, and generating summaries of long texts. They will also understand the limitations of NLP models and how to approach complex NLP tasks effectively.

1. 1. Introduction to Natural Language Processing (NLP): Learners will explore the basics of NLP, including its definition, applications, and the importance of text processing. They will gain foundational skills in understanding and utilizing NLP techniques in practical scenarios.
2. 2. Python Programming for Text Processing: This module covers essential Python programming skills for text processing, including string manipulation, data structures, and basic programming paradigms. Learners will write simple scripts to process text data.
3. 3. Text Preprocessing Techniques: Learners will study various text preprocessing techniques such as tokenization, stemming, lemmatization, and stop word removal. They will learn to clean and prepare text data for further analysis using Python.
4. 4. Text Classification with Python: This module focuses on text classification techniques using Python. Learners will implement and evaluate classification models for text categorization tasks, gaining hands-on experience with libraries like scikit-learn.
5. 5. Sentiment Analysis using Python: Learners will delve into sentiment analysis, learning how to determine the emotional tone behind words in a piece of text. They will develop practical skills in building and deploying sentiment analysis models.
6. 6. Named Entity Recognition (NER) with Python: This module covers Named Entity Recognition (NER), focusing on identifying and extracting named entities from text. Learners will work on NER tasks using Python and popular NLP libraries.
7. 7. Text Generation and Prediction: Learners will explore text generation techniques and predictive modeling with text data. They will implement models to generate new text based on existing datasets, enhancing their understanding of NLP in creative applications.
8. 8. Advanced NLP Techniques and Libraries: This module introduces advanced NLP techniques and explores more specialized libraries and tools. Learners will gain deeper insights into cutting-edge NLP methods and their practical applications.
9. 9. Natural Language Generation (NLG) with Python: Focusing on NLG, learners will create models that can generate human-like text based on given inputs. They will develop skills in using Python for text generation across various domains.
10. 10. Project: Building a Text Processing Pipeline: In this final module, learners will apply all the skills acquired throughout the programme by building a comprehensive text processing pipeline. They will work on a real-world project to process, analyze, and generate text data.