Undergraduate Certificate in Python AI Programming: Natural Language Processing

This course is designed for undergraduate students with a foundational knowledge of Python programming who wish to specialize in natural language processing (NLP). It equips students with essential skills in building NLP models, processing and analyzing textual data, and applying these techniques to real-world problems. Students will gain proficiency in using Python libraries such as NLTK, spaCy, and TensorFlow, and will learn to implement NLP algorithms for tasks like text classification, sentiment analysis, and language generation.

Upon completion, students will have a solid understanding of NLP methodologies and practical experience in developing NLP applications, preparing them for careers in data science, artificial intelligence, and related fields.

1. 1. Introduction to Python and AI Fundamentals: Learners will study the basics of Python programming and key AI concepts, gaining foundational skills necessary for AI development. They will write simple Python scripts and understand the role of AI in modern applications.
2. 2. Natural Language Processing (NLP) Basics: This module covers the fundamental principles of NLP, including text preprocessing, tokenization, and basic text analysis techniques, enabling learners to work with raw text data effectively.
3. 3. Text Processing with NLTK: Learners will explore the Natural Language Toolkit (NLTK) for text processing, learning how to perform tasks such as part-of-speech tagging, stemming, and lemmatization, and how to handle large text corpora.
4. 4. Sentiment Analysis with Python: This module focuses on developing sentiment analysis models using Python. Learners will understand how to preprocess text data for sentiment analysis, select appropriate algorithms, and evaluate model performance.
5. 5. Text Classification and Machine Learning: Learners will delve into text classification techniques using machine learning, including supervised and unsupervised methods. They will gain hands-on experience with algorithms like Naive Bayes, SVM, and decision trees.
6. 6. Deep Learning for NLP: This module introduces deep learning models for NLP, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers. Learners will implement these models using frameworks like TensorFlow or PyTorch.
7. 7. Named Entity Recognition (NER): Learners will study NER techniques for identifying and extracting named entities from text, such as people, organizations, and locations. They will implement and evaluate NER models using various datasets.
8. 8. Conversational AI and Chatbots: This module covers the development of conversational AI systems and chatbots using Python. Learners will learn about dialogue management, natural language generation, and integration with APIs.
9. 9. Text Summarization: Learners will explore techniques for automatic text summarization, including extractive and abstractive methods. They will implement summarization models and evaluate their effectiveness for different types of text.
10. 10. Final Project: NLP Application: In this capstone module, learners will design and implement a complete NLP application, applying the skills and knowledge gained throughout the programme. They will present their project, demonstrating their ability to solve real-world NLP problems.