Executive Development Programme in Python NLP: Data Preprocessing and Feature Extraction

This course is designed for executives and business leaders aiming to leverage Python Natural Language Processing (NLP) for data analysis and decision-making. Participants will learn essential skills in data preprocessing, including text cleaning, normalization, and tokenization, as well as advanced feature extraction techniques to enhance model performance.

By the end of the programme, participants will be able to preprocess and prepare text data effectively for NLP applications, gaining the capability to improve the accuracy and reliability of their data-driven strategies.

1. 1. Introduction to Python and NLP: Learners will understand the basics of Python programming and key Natural Language Processing (NLP) concepts, including text data handling and basic text preprocessing tasks. They will gain foundational programming skills and learn to install and use essential NLP libraries.
2. 2. Text Data Cleaning and Preprocessing: This module covers text normalization, removing noise, and handling missing data. Learners will master techniques like tokenization, stop words removal, and stemming/lemmatization, preparing text data for further processing.
3. 3. Text Tokenization and Vectorization: Learners will study different tokenization methods and understand the importance of vectorization in NLP. They will practice using techniques such as Bag of Words, TF-IDF, and word embeddings to convert text into numerical formats suitable for machine learning models.
4. 4. Advanced Text Preprocessing Techniques: This module delves into more sophisticated preprocessing techniques, including n-grams, text filtering, and custom preprocessing pipelines. Learners will learn to create and optimize preprocessing workflows for better model performance.
5. 5. Feature Extraction from Text Data: Learners will explore various feature extraction methods beyond simple vectorization, including topic modeling (e.g., LDA) and sentiment analysis. They will gain skills in extracting meaningful features that capture the essence of text data.
6. 6. Handling Text Data Challenges: This module focuses on addressing common challenges in NLP, such as handling varying text lengths, dealing with imbalanced datasets, and managing text data in large-scale applications. Learners will learn practical strategies to overcome these issues.
7. 7. Advanced Vectorization Techniques: Learners will study advanced vectorization techniques, including contextual embeddings (e.g., BERT, ELMo) and how to use them in NLP tasks. They will understand the importance of contextual understanding in NLP and learn to implement these models effectively.
8. 8. Feature Engineering for NLP: This module covers the process of designing and implementing features that are tailored to specific NLP tasks. Learners will practice feature engineering techniques to improve model performance and gain insights into the data.
9. 9. Evaluating and Selecting Features: Learners will learn how to evaluate the effectiveness of different features and select the best ones for their NLP projects. They will use statistical methods and machine learning metrics to compare and choose optimal features.
10. 10. Project: End-to-End Text Preprocessing and Feature Extraction: In this final module, learners will apply all the skills learned throughout the programme to a comprehensive project. They will preprocess a real-world text dataset, extract relevant features, and prepare the data for further analysis or machine learning model training.