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In the previous article, we explained how AI is a future skill and explained the role of commanding salary of up to $375,000 a year.
Large-scale language models (LLM) are the central focus of AI, and almost all data-centric roles require a basic understanding of these algorithms.
Whether you're a developer looking to expand their skillset, data practitioner, or professionals looking to move into the field of AI, you can get a lot from learning about LLMS in the current job market.
This article offers 10 free resources to help you learn about large-scale language models.
1. Andrej Karpathy introduces a large-scale language model
If you are a complete beginner in the field of AI, we recommend starting with this one-hour YouTube tutorial explaining how LLMS works.
By the end of this video, you will understand the working behind LLMS, LLM scaling methods, model tweaks, multimodality, and LLM customizations.
2. genai for beginners by Microsoft
Generation AI for Beginners is an 18-lecture course that teaches you everything you need to know about building a Generation AI application.
It starts with the very basics – first we introduce the concepts of generation AI and LLM, then we move on to topics like prompt engineering and LLM selection.
Next, you will learn to build LLM-driven applications using low-code tools, lags, and AI agents.
This course also teaches you how to fine-tune your LLMS to protect your LLM applications.
You can skip modules and choose the lessons that are most relevant to your learning goals.
3. Genai with LLMS by deeplearning.ai
Generation AI with LLMS is a course on language models that conducts full-time research for approximately three weeks.
This learning resource covers the fundamentals of LLMS, trans-architecture, and rapid engineering.
You will also learn to fine-tune, optimize and deploy language models in AWS.
4. Hugging the Face NLP course
Hugging Face is a leading NLP company that provides libraries and models that allow you to build machine learning applications. It makes it easy for everyday users to create AI applications.
Embracing Face's NLP Learning Track covers trans architecture, working behind LLMS, and datasets and talker libraries available within the ecosystem.
Learn to fine-tune your dataset, perform tasks like text summaries, question answers, and translation using the Transformers library, and hug your face pipeline.
5. LLM University by Cohere
LLM University is a learning platform that covers concepts related to NLP and LLMS.
As with previous courses on this list, we will start by learning about the basics of LLMS and its architecture and moving on to more advanced concepts such as rapid engineering, fine-tuning, and lag.
If you already have knowledge of NLP, you can skip the basic modules and follow a more advanced tutorial.
6. Basic generation AI using Ineron
Foundational Generic AI is a free, two-week course covering the basics of Generated AI, Langchain, Vector database, Open Source Language Models, and LLM deployment.
Each module takes about 2 hours to complete, and it is recommended that each module be completed in one day.
By the end of this course, you will learn to implement end-to-end medical chatbots using a language model.
7. Natural Language Processing by Krish Naik
This NLP playlist on YouTube covers concepts such as tokenization, text preprocessing, RNN, LSTM and more.
These topics are prerequisites for understanding how today's large language models work.
After taking this course, you will be able to understand the various text processing techniques that form the backbone of NLP.
It also understood the working behind the sequential NLP model and the challenges faced with their implementation, which ultimately led to the development of more advanced LLMs, such as the GPT series.
Additional LLM Learning Resources
Some additional resources for learning LLM include:
1. Coded paper
Papers with Code is a platform that combines ML research papers and code, making it easier to keep up with the latest developments in this field, along with practical applications.
2. Care must be taken
To better understand transformer architectures (the foundations of cutting-edge language models such as Bert and GPT), we recommend reading a research paper titled “Caution is required.”
This gives us a better understanding of how LLMS works and why transformer-based models perform significantly better than previous cutting edge models.
3. LLM-POWERHOUSE
This is a GitHub repository that curates LLM tutorials, best practices, and code.
This is a comprehensive guide to language models with a detailed description of the LLM architecture, tutorials on model tweaking and deployment, and code snippets that can be used directly in your own LLM applications.
10 Free Resources for Learning LLMS – Important Points
There is an ocean of resources available to learn LLMS, and we've put together some of the most useful items in this article.
Most of the study materials cited in this article require knowledge of coding and machine learning. If you don't have any background in these areas, we recommend that you look into the following resources:
 
 
Natach Selbalaji He is a self-taught data scientist with a passion for writing. Natassha writes about all data science-related, a true master of all data topics. Connect with her on LinkedIn or check out her YouTube channel.
