Executive Development Programme in Text Preprocessing and Feature Extraction in Python

This course is designed for executives and professionals seeking to enhance their data preprocessing and feature extraction skills using Python. Participants will gain hands-on experience with advanced text processing techniques, enabling them to improve the quality and efficiency of data-driven decision-making processes.

By the end of the program, attendees will be proficient in using Python libraries for text cleaning, normalization, and extraction of meaningful features from textual data, ready to apply these skills in their organizations to boost performance and innovation.

1. 1. Introduction to Text Preprocessing: Learners will study the importance of text preprocessing and foundational techniques such as tokenization, stop words removal, and stemming. They will gain practical skills in preparing text data for analysis using Python libraries like NLTK and spaCy.
2. 2. Text Normalization Techniques: This module covers text normalization techniques including case folding, punctuation removal, and encoding conversions. Learners will learn to normalize text data effectively to improve the accuracy of subsequent processing steps.
3. 3. Feature Extraction Methods: Learners will explore various feature extraction methods such as Bag of Words, TF-IDF, and word embeddings. Practical skills include using these techniques to represent text data for machine learning models.
4. 4. Advanced Text Normalization: This module delves into more advanced normalization techniques like lemmatization and custom tokenization. Learners will gain the ability to apply these techniques to handle complex text data.
5. 5. Handling Text Corpora: The focus is on managing large text corpora, including data cleaning, filtering, and sampling. Learners will practice processing and analyzing large datasets efficiently using Python.
6. 6. Text Classification with Preprocessing: Learners will study how to preprocess text data and apply it to text classification tasks. Practical skills include using preprocessing techniques to improve the performance of machine learning classifiers.
7. 7. Feature Engineering for Text Data: This module covers advanced feature engineering techniques for text data, including n-grams, part-of-speech tagging, and entity recognition. Learners will gain skills in creating rich feature sets for text analysis.
8. 8. Text Clustering and Dimensionality Reduction: Learners will explore text clustering techniques and dimensionality reduction methods like LDA and PCA. Practical skills include applying these techniques to discover hidden patterns in text data.
9. 9. Handling Text Data with Databases: This module focuses on storing and querying text data using databases. Learners will learn to efficiently manage and analyze text data stored in databases.
10. 10. Real-World Project in Text Preprocessing and Feature Extraction: In this final module, learners will work on a comprehensive project that involves preprocessing and feature extraction of a real-world text dataset. They will apply all the skills learned throughout the programme to solve a practical problem.