Global Certificate in Developing Language Models with Python and NLP

This course is designed for data scientists, machine learning engineers, and software developers interested in building advanced language models. Participants will gain hands-on experience with Python and key Natural Language Processing (NLP) techniques, enabling them to create, train, and optimize language models for various applications.

Course participants will learn to leverage popular NLP libraries and frameworks, understand model architecture, and apply them to real-world problems. By the end, they will have developed a robust portfolio of projects, enhancing their skills in text processing, sentiment analysis, and more, making them proficient in the development of language models using Python.

1. 1. Introduction to Natural Language Processing (NLP): Learners will understand the basics of NLP, its applications, and the importance of text data in modern technology. They will gain foundational skills in text preprocessing, including tokenization, stemming, and lemmatization.
2. 2. Python for NLP: This module covers essential Python libraries for NLP, such as NLTK and spaCy, and introduces learners to writing basic scripts for text manipulation and analysis.
3. 3. Machine Learning Basics for NLP: Learners will explore fundamental machine learning concepts and techniques, focusing on their application to NLP tasks. They will gain hands-on experience with supervised and unsupervised learning methods.
4. 4. Building Text Classifiers: This module focuses on training and evaluating text classifiers using various algorithms. Learners will practice building models for text classification tasks and learn about feature extraction techniques.
5. 5. Sentiment Analysis with NLP: Learners will study methods for sentiment analysis, including the use of lexicons and machine learning models. They will gain practical skills in analyzing and interpreting sentiment in texts.
6. 6. Text Generation with Recurrent Neural Networks: This module introduces recurrent neural networks (RNNs) and long short-term memory (LSTM) networks for generating text. Learners will build and train text generation models on various datasets.
7. 7. Transformer Models for NLP: Learners will delve into transformer models, a key architecture for state-of-the-art NLP tasks. They will learn how to implement and fine-tune transformer models for tasks such as translation and text summarization.
8. 8. Advanced NLP Techniques: This module covers advanced NLP techniques, including named entity recognition, part-of-speech tagging, and dependency parsing. Learners will gain expertise in using these techniques for various NLP applications.
9. 9. Implementing Language Models: Learners will learn how to design and implement language models for tasks such as text completion and language translation. They will gain experience with training large-scale models on parallel computing resources.
10. 10. Case Studies and Final Project: In this module, learners will apply their knowledge to real-world NLP projects. They will work on case studies and complete a final project that demonstrates their ability to develop and implement language models using Python and NLP techniques.