Executive Development Programme in Practical NLP with Python: Building Language Models

This course is designed for executives and professionals with a basic understanding of Python who wish to leverage natural language processing (NLP) for business insights. Participants will gain hands-on skills to build and deploy language models, analyze text data, and enhance decision-making processes using practical NLP techniques.

You will learn to preprocess textual data, implement sentiment analysis, perform topic modeling, and build predictive models. The course emphasizes real-world applications and equips you with the knowledge to integrate NLP into your organization's strategic initiatives.

1. 1. Introduction to Natural Language Processing (NLP): Learners will understand the basics of NLP, its applications, and the foundational concepts including tokenization, stemming, and lemmatization. They will gain practical skills in using Python libraries like NLTK for text preprocessing.
2. 2. Core NLP Techniques and Libraries: This module covers essential NLP techniques such as Part-of-Speech tagging, named entity recognition, and sentiment analysis. Learners will explore popular NLP libraries such as spaCy and gain hands-on experience with these tools.
3. 3. Building a Text Classification Model: Learners will learn how to build and train a text classification model using machine learning techniques. They will gain practical skills in using scikit-learn for model training and evaluation, and will understand the process of text vectorization.
4. 4. Advanced Text Vectorization Techniques: This module delves into advanced text vectorization methods such as TF-IDF and word embeddings like Word2Vec and GloVe. Learners will gain expertise in using these methods for better model performance and will implement them in Python.
5. 5. Creating a Named Entity Recognition System: Learners will develop a Named Entity Recognition (NER) system using deep learning techniques and libraries like TensorFlow or PyTorch. They will learn to train and fine-tune models on NER datasets and understand the underlying neural network architectures.
6. 6. Sentiment Analysis with Deep Learning: This module focuses on using deep learning for sentiment analysis tasks. Learners will build models using recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, and gain practical skills in preprocessing text data for these models.
7. 7. Generating Text with Language Models: Learners will explore the basics of language modeling, including n-grams and hidden Markov models (HMMs). They will then move on to more advanced models like RNNs and transformers for generating coherent text.
8. 8. Fine-Tuning Pre-trained Language Models: This module covers fine-tuning large pre-trained language models such as BERT and T5 for various NLP tasks. Learners will understand the importance of transfer learning and gain practical experience in fine-tuning models using Hugging Face's transformers library.
9. 9. Advanced Topic: Conversational Agents and Chatbots: In this module, learners will learn how to build conversational agents and chatbots using NLP techniques. They will gain skills in designing conversation flows, implementing dialogue management, and integrating models with natural language understanding and generation.
10. 10. Project: Building a Comprehensive NLP Application: For the final project, learners will work on building a comprehensive NLP application that integrates multiple NLP techniques and models. They will apply all the skills learned throughout the programme to create a functional NLP application and will present their project to peers and instructors.