Structure of the Course
NLP University has three main modules, and two appendices. Let me tell you about these one by one. But before, a bit about the structure of the course.
Ways to learn
The material we’ve put together can be used in two ways:
- Sequential: If you want to start from the very beginning, the course will take you through the basics of Large Language Models and their architecture, and then a lot of practice, including coding, and prompt engineering. The course starts with Large Language Models and requires very little background knowledge. However, if you'd like to brush up on the basics of Machine Learning and Natural Language Processing, this material is in Appendix 1.
- Non-Sequential: If you feel like you already know the basics of NLP and LLMs, or you have a particular project in mind, you can skip ahead to the later modules. If you have a specific goal in mind, you are more than welcome to go directly to that particular module.
Main Modules
On these modules you'll learn what LLMs are, how they work, and then get a practical hands-on labs for you to build your own language applications. The first module is focused on theory, while modules 2, 3, and 4 are a combination of theory and hands-on practice with code labs.
Module 1 gives you the fundamentals of LLMs. This module is geared towards understanding the architecture of transformers models, including embeddings, similarity, and attention, and some of their applications such as semantic search.
Module 2 is an introduction to Cohere’s endpoints. It is focused on those endpoints that are used for text representation, such as embed, classify, and summarize. In this module you’ll find several codelabs guided towards building cool applications such as clustering news, classifying text, or semantic search.
Since generation is so important and has been so relevant lately, this module is focused solely on generation. A very important part of generation is prompting, namely, being able to give the model the correct question or command in order to generate what you want. This is all done in the Cohere playground, so there’s no code involved. The second part then delves into the code, and teaches you how to use the generate endpoint.
Extra Material
We have also gathered some extra material to complement your learning.
If you feel like you need a brush up on the basics on NLP or ML, Appendix 1 has the material to get you up to speed.
This module which contains an introduction to natural language processing, including history, language pre-processing techniques, and the machine learning models that have been used since NLP started, including supervised and unsupervised techniques. It ends with classification, including training classification models, evaluating them, and exploring their applications.
And if after going through the course, you'd like some extra practice on building apps, Appendix 2 has links to several code labs which will show you how to build some very cool apps, such as recommenders, question/answering bots, and more!
If you want to get more practice into building bigger applications, we include a series of code labs in which you can find a plethora of demos combining different endpoints to build some really cool applications, such as article recommenders, invoice extractors, lazy writers, name generators, question answering models, etc. Some of these have also been deployed as applications using different platforms.
Ready to learn? Go!
We are so excited to have you learning with us. Happy learning!
Updated 6 days ago