Executive Development Programme in Python NLP: Building and Evaluating Language Models

This course, designed for business leaders and data professionals, equips participants with the skills to build and evaluate Natural Language Processing (NLP) models using Python. Participants will gain proficiency in preprocessing text data, selecting appropriate NLP techniques, and deploying models for real-world applications.

Attendees will learn to measure model performance using relevant metrics and understand the ethical considerations in NLP. By the end, they will be able to lead data science initiatives that leverage NLP to drive strategic decisions in their organizations.

1. 1: Introduction to Python and NLP: Learners will be introduced to Python programming and Natural Language Processing (NLP) basics. They will gain foundational skills in setting up development environments and working with text data.
2. 2: Text Preprocessing and Cleaning: This module covers techniques for cleaning and preprocessing text data, including tokenization, stemming, lemmatization, and stop words removal. Learners will gain practical skills in preparing text data for NLP tasks.
3. 3: Building Text Vectorization Models: Learners will study and implement various text vectorization methods such as Bag of Words, TF-IDF, and word embeddings. Practical skills include using libraries like Scikit-learn and Gensim.
4. 4: Introduction to Language Models: This module introduces the concept of language models and their applications. Learners will explore foundational models like n-grams and understand how they can be used for text generation and classification tasks.
5. 5: Advanced Language Models: Recurrent Neural Networks (RNNs): Building on foundational knowledge, this module delves into RNNs, specifically Long Short-Term Memory (LSTM) networks, and their application in sequence prediction tasks. Practical skills include implementing RNNs using TensorFlow or PyTorch.
6. 6: Attention Mechanisms and Transformers: This module covers attention mechanisms and their application in transformer models. Learners will gain practical experience in building and training transformer models using Hugging Face Transformers library.
7. 7: Evaluating Language Models: This module focuses on various metrics for evaluating language models, including perplexity, BLEU score, and human evaluation. Learners will learn how to use these metrics to assess model performance and make informed decisions.
8. 8: Case Studies and Applications: In this module, learners will work on real-world case studies, applying the knowledge from previous modules to build and evaluate NLP models for tasks such as sentiment analysis, text classification, and chatbot development.
9. 9: Deploying NLP Models: This module covers the deployment of NLP models in production environments. Learners will learn about model packaging, deployment strategies, and integrating NLP models into web applications or APIs.
10. 10: Advanced Topics in NLP: This final module introduces advanced topics in NLP, such as transfer learning, multi-modal NLP, and ethical considerations in NLP. Learners will gain insight into cutting-edge research and trends in the field.