If you are aiming for a career in Natural language Processing (NLP), learning about Large Language Models (LLMs) is a must for you now. Although you won’t use LLMs as a fresher, you will find LLMs in the job description of every job based on NLP, even if there’s no use of it in their business. So, if you are looking for a guide on how to learn NLP and LLMs, this article is for you. In this article, I’ll take you through a complete roadmap to learn NLP and LLMs with learning resources.
Roadmap to learn NLP and LLMs
Here’s a complete step-by-step roadmap to learn NLP and LLMs with learning resources.
Learn Python to Work with Data
Master Python programming, focusing on its application in data manipulation and analysis. Python is the most popular language for data science and NLP due to its readability, vast ecosystem of libraries, and active community. Focus on learning how to handle data with libraries like NumPy, pandas, and Matplotlib for visualization.
Here are some resources you can follow to learn Python to work with data:
- A Free Course on the Fundamentals of Python by Udacity
- Data Analysis Free Course by Udacity using Numpy and Pandas
- Matplotlib Guide
Fundamentals of NLP
Understand the basic principles, techniques, and algorithms used in NLP. Learn how computers process and analyze human language. Study topics like tokenization, stemming, lemmatization, part-of-speech tagging, and Named Entity Recognition (NER).
Here are some resources you can follow to learn the fundamentals of NLP:
Machine Learning Fundamentals
Learning the fundamentals of Machine Learning is important for NLP as well. Understand supervised and unsupervised learning models, including linear regression, logistic regression, decision trees, and clustering. Focus on how these models can be applied to NLP tasks like text classification and sentiment analysis.
Here are some resources you can follow to learn the fundamentals of Machine Learning:
Deep Learning for NLP
Learn about neural networks and how they are applied to NLP tasks. Explore more complex models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) for processing text data.
Here are some resources you can follow to learn Deep Learning for NLP:
Advanced NLP with Deep Learning
Dive into more sophisticated NLP techniques powered by deep learning. Study advanced topics such as attention mechanisms, Transformer models, and techniques for handling large-scale NLP problems.
Here are some resources you can follow to learn advanced NLP with Deep Learning:
Explore Large Language Models (LLMs)
Understand and experiment with large-scale models like GPT, BERT, and T5. Learn the architecture and applications of these models, focusing on how they can be fine-tuned and used for a variety of NLP tasks.
Here are some resources you can follow to learn about Large Language Models:
So, this is how you can learn NLP and LLMs step by step.
Work on Projects
In the end, apply the knowledge you’ve gained through hands-on projects. Choose projects that interest you and can showcase your skills, such as building a chatbot, developing a sentiment analysis tool, or creating a machine translation system.
Here are some projects you can work on:
- Document Analysis using LLMs
- Text Summarization Model with LLMs
- Text Generation with LLMs
- Code Generation Model
- Text Generation Model
- Next Word Prediction Model
- End-to-End Chatbot
Summary
If you are aiming for a career in Natural language Processing (NLP), learning about Large Language Models (LLMs) is a must for you now. I hope this roadmap will help you in learning NLP and LLMs step by step. I hope you liked this article on a complete roadmap to learn NLP and LLMs. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
