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In a previous article, I explained how ai is the skill of the future, with roles commanding salaries of up to $375,000 per year.
Large language models (LLMs) have become a central focus in ai, and nearly all data-centric roles now require a basic understanding of these algorithms.
Whether you are a developer looking to expand your skill set, a data professional, or a professional looking to transition into the ai field, you can gain many benefits from learning about LLMs in today’s job market.
In this article, I will provide you with 10 free resources that will help you learn about large language models.
1. Introduction to the major linguistic models by Andrej Karpathy
If you are an absolute beginner in the field of ai, I recommend starting with this. One hour long YouTube tutorial explaining how LLMs work.
By the end of this video, you will understand how LLMs work, LLM scaling laws, model fine-tuning, multimodality, and LLM customization.
2. GenAI for Beginners from Microsoft
ai-for-beginners/#/” target=”_blank” rel=”noopener”>Generative ai for Beginners is an 18-lesson course that will teach you everything you need to know about building generative ai applications.
It starts from the very basics: you will first be introduced to the concept of generative ai and LLM, and then move on to topics like rapid engineering and LLM selection.
You will then learn how to build LLM-powered applications using low-code tools, RAGs, and ai agents.
The course will also teach you how to perfect your LLM and protect your LLM applications.
You are free to skip modules and select the lessons that are most relevant to your learning goals.
3. GenAI with Deeplearning.ai LLM
ai/courses/generative-ai-with-llms/” target=”_blank” rel=”noopener”>Generative ai with LLM This is a course on language models that will last approximately 3 weeks of full-time study.
This learning resource covers the basics of LLM, Transformer Architecture and Rapid Engineering.
You will also learn how to tune, optimize, and deploy language models on AWS.
4. NLP Course “Hugging the Face”
Hugging Face is a leading natural language processing company that provides libraries and models to build machine learning applications. They enable everyday users to easily build ai applications.
Hugging Face NLP Learning Path covers the architecture of the transformer, the workings behind LLMs, and the datasets and tokenizer libraries available within its ecosystem.
You will learn how to fit datasets and perform tasks such as text summarization, question answering, and translation using the Transformers library and the Hugging Face pipeline.
5. LLM University by Cohere
LLM University It is a learning platform that covers concepts related to NLP and LLM.
Similar to the previous courses on this list, you will start by learning the basics of LLMs and their architecture, and progress to more advanced concepts like rapid engineering, fine-tuning, and RAG.
If you already have some knowledge of NLP, you can simply skip the basic modules and follow the more advanced tutorials.
6. iNeuron's Fundamental Generative ai
ai/course/generative-ai-community-edition” target=”_blank” rel=”noopener”>Fundamental generative ai is a free 2-week course covering the basics of generative ai, Langchain, vector databases, open-source language models, and LLM implementation.
Each module takes approximately two hours to complete and it is recommended that each module be completed in one day.
By completing this course, you will learn how to implement an end-to-end medical chatbot using a language model.
7. Natural Language Processing by Krish Naik
This NLP playlist on YouTube covers concepts such as tokenization, text preprocessing, RNNS and LSTM.
These topics are prerequisites for understanding how large language models work today.
After taking this course, you will understand the different text processing techniques that form the backbone of NLP.
You will also understand the working of sequential NLP models and the challenges faced in implementing them, which eventually led to the development of more advanced LLMs like the GPT series.
Additional learning resources for the LLM
Some additional resources for learning LLM include:
1. Documents with code
Documents with code is a platform that combines ML research articles with code, allowing you to stay up to date with the latest developments in the field along with practical applications.
2. Attention is all you need
To better understand the Transformer architecture (the foundation of state-of-the-art language models like BERT and GPT), I recommend reading the research paper titled “Attention is all you need”.
This will give you a better understanding of how LLMs work and why transformer-based models perform significantly better than older state-of-the-art models.
3. Master of Laws (LLM) PowerHouse
This is a GitHub repository which curates LLM tutorials, best practices, and code.
It is a comprehensive guide to the language model, with detailed explanations of the LLM architecture, tutorials on model tuning and implementation, and code snippets that can be used directly in your own LLM applications.
10 Free Resources to Learn How to Earn a Master of Laws Degree: Key Takeaways
There are a sea of resources available to learn how to obtain an LLM, and I have compiled the most useful ones in this article.
Most of the learning material cited in this article requires some knowledge of coding and machine learning. If you don't have experience in these areas, I recommend checking out the following resources:
 
 
Natassha Selvaraj Natassha is a self-taught data scientist with a passion for writing. She writes about everything related to data science, she is a true master of all things data-related. You can contact her at LinkedIn or take a look at it YouTube Channel.
