Executive Development Programme in Hands-On Python NLP: Tokenization and Parsing

This course is tailored for executives and managers seeking to enhance their data analysis skills through practical Python Natural Language Processing (NLP) techniques. Participants will gain hands-on experience in tokenization and parsing, essential for extracting meaningful insights from textual data.

By the end of the program, attendees will be proficient in using Python libraries such as NLTK and SpaCy to preprocess text data, enabling them to improve decision-making processes based on text analytics.

1. 1. Introduction to Python NLP: Learners will understand the basics of Natural Language Processing (NLP) and how Python is used in this field. They will gain foundational skills in setting up a Python environment and working with text data.
2. 2. Text Preprocessing and Tokenization: This module covers the importance of text preprocessing and introduces tokenization techniques. Learners will learn how to clean text data, split sentences into tokens, and gain hands-on experience using Python libraries like NLTK and spaCy.
3. 3. N-Gram Models for Sequence Analysis: Learners will explore N-gram models and their applications in sequence analysis. They will gain practical skills in generating and analyzing bigrams, trigrams, and higher-order n-grams using Python.
4. 4. Part-of-Speech Tagging and Lemmatization: This module focuses on part-of-speech tagging and lemmatization techniques. Learners will understand these concepts and practice using them to analyze and normalize text data, enhancing their text processing skills.
5. 5. Dependency Parsing and Constituency Parsing: Learners will delve into dependency parsing and constituency parsing to understand sentence structure. They will gain experience in using Python to parse sentences and extract syntactic relations, improving their ability to analyze complex text.
6. 6. Named Entity Recognition (NER) with Python: This module covers Named Entity Recognition (NER) techniques. Learners will learn how to identify and extract named entities from text using Python libraries like Spacy and Hugging Face Transformers.
7. 7. Text Classification and Sentiment Analysis: Learners will study text classification and sentiment analysis methods. They will gain practical skills in building models to classify text into different categories and analyze sentiment, using machine learning libraries like scikit-learn and TensorFlow.
8. 8. Advanced Tokenization Techniques: This module explores advanced tokenization techniques, including subword models and byte-pair encoding. Learners will learn how to implement these methods and understand their importance in NLP tasks.
9. 9. Parsing Complex Sentences and Dialogue: Learners will focus on parsing complex sentences and dialogue structures. They will gain practical skills in handling more intricate text data and applying advanced parsing techniques using Python.
10. 10. Project: Building an NLP Pipeline: In this capstone project, learners will design and implement an NLP pipeline that incorporates various techniques learned throughout the course. They will gain valuable experience in applying their skills to a real-world NLP problem.