Postgraduate Certificate in Text Preprocessing Techniques with Python

This course is designed for data scientists, machine learning engineers, and postgraduate students who need to preprocess text data for natural language processing tasks. Participants will gain proficiency in using Python for text cleaning, normalization, tokenization, stop-word removal, stemming, and lemmatization, essential skills for preparing text data for analysis.

Students will also learn to implement these techniques using popular Python libraries such as NLTK, spaCy, and Scikit-learn. By the end of the course, they will be able to preprocess text data effectively, improving the performance of their NLP models and gaining practical, industry-relevant skills.

1. 1. Introduction to Text Data and Preprocessing: Learners will study the nature of text data, common challenges in handling it, and foundational text preprocessing techniques. They will gain skills in text cleaning, tokenization, and basic text normalization.
2. 2. Text Cleaning and Normalization: This module focuses on removing unwanted text elements, standardizing text format, and preparing text for analysis. Learners will master techniques such as removing punctuation, handling special characters, and converting text to lowercase.
3. 3. Tokenization and Text Segmentation: Learners will learn about different tokenization methods and text segmentation techniques, including sentence splitting and word tokenization. Practical skills include using Python libraries like NLTK and spaCy for efficient text segmentation.
4. 4. Stemming and Lemmatization: This module covers advanced text normalization techniques, teaching learners how to reduce words to their base or root form. Practical exercises will involve implementing stemming and lemmatization using libraries like NLTK and spaCy.
5. 5. Text Vectorization Techniques: Learners will explore various methods to convert text data into numerical form, including Bag-of-Words, TF-IDF, and word embeddings. Practical skills include using scikit-learn and spaCy for text vectorization.
6. 6. Handling Missing and Noisy Data: This module addresses strategies for dealing with missing or noisy text data, including imputation techniques and data cleaning methods. Practical exercises will involve cleaning and processing raw text datasets.
7. 7. Text Classification with Python: Learners will study and implement text classification models using Python, focusing on techniques like Naive Bayes, SVM, and decision trees. Practical skills include building and evaluating text classifiers using scikit-learn.
8. 8. Sentiment Analysis and Opinion Mining: This module covers advanced text analysis techniques, specifically focusing on sentiment analysis and opinion mining. Practical skills include preprocessing text for sentiment analysis and building models to classify sentiment.
9. 9. Text Summarization and Clustering: Learners will learn about text summarization techniques and text clustering methods. Practical skills include implementing text summarization and clustering using libraries like Gensim and scikit-learn.
10. 10. Advanced Text Preprocessing with NLP Frameworks: This module introduces learners to advanced NLP frameworks and libraries for text preprocessing, such as TensorFlow and PyTorch. Practical skills include building complex preprocessing pipelines and training neural networks for text processing tasks.