Generative AI, a field brimming with endless possibilities, is poised to reshape numerous industries through its groundbreaking advancements. To embark on this transformative journey, multiple organizations are offering exceptional courses that delve into the fundamentals of Generative AI, exploring its diverse applications, challenges, and ethical considerations, and the best part is that they are completely free.

The very point of this blog is to help break down the whole process into something a little more easily digestible and expose an exhaustive list of the best resources for gaining experience in GenAI, hoping it will come in handy for some if not all.

Defining Large Language Models, also known as LLMs

From a layman's perspective, we can define a Large Language Model (LLM) as a type of artificial intelligence (AI) that can generate text, translate languages, and answer your questions in an informative way, whereas from a developer's or let's say Machine Learning Practitioner's perspective we can define it as language model possessing an unprecedented level of generality, resulting from the combination of scaling and attention-based neural architectures.

LLMs are trained on massive amounts of text data, which allows them to learn the patterns and structures of language. This makes them capable of producing human-like text, translating languages accurately, content creation, sentiment analysis, and answering questions in a comprehensive and informative way.

The Evolution of LLMs: From GPT to GPT-4

LLMs have come a long way since their inception. One of the most notable series of LLMs is the GPT (short for “Generative Pre-trained Transformer”) series developed by OpenAI. Starting with GPT, each iteration has improved upon the previous one, leading to more powerful and versatile models. What made the GPT model so special was that it was one of the first language models to use the Transformer architecture. This is a type of neural network that's great at understanding long-range dependencies in text data, which made it possible for the model to generate highly coherent and contextually relevant language output. With 117 million parameters, the GPT model was a real game-changer for natural language processing. Since then, we've seen the development of even larger and more impressive language models, like GPT-2, GPT-3, and BERT

The latest version, GPT-4, is an incredibly advanced model that can perform a wide range of language tasks with impressive accuracy. It has been trained on vast amounts of data, which allows it to generate highly relevant and contextually appropriate responses.

Apart from the GPT series developed by OpenAI, there are several other notable Large Language Models including, Google’s PaLM, Meta’s LLaMA family of models, and many other open-source fine-tuned LLMs at present.

Why should you care about this trend?

ChatGPT is probably the best tool launched in this decade and all the hype since it launched in November last year is for real. Indeed, ChatGPT has taken the world by storm 💹. Within a week of its launch, it had 1 million users.

In simple terms, It's gonna make our lives easier from major perspectives, if not all, as it has its challenges and risks.

With its ability to process large amounts of text data and versatility, LLMs can be used for a wide range of applications, from chatbots that can hold conversations with users to content-generation tools that can write articles or product descriptions.

Large Language models can help reduce the time and cost of developing NLP applications as well. After all, as the saying goes "The sky's the limit".

In this article, I have curated a list of the best free GenAI courses for 2023. These courses cover a wide range of topics, from understanding the fundamentals of LLMs to advanced techniques for leveraging their potential in various applications.

Whether you are a beginner or an experienced user, these courses are designed to equip you with the skills and knowledge needed to excel in working with Large Language Models.

Without any further due, Let’s dive in and discover the best Generative AI(LLM) courses to enhance your skills and stay ahead in this exciting field!

List of Free Courses

1. Generative AI Learning Path By Google Cloud

   Recently, Google released an incredible opportunity to dive into the world of Generative AI, through their Google Cloud Skills Boost Learning Paths, with Seven new no-cost generative AI training courses to advance your cloud career. And the best part? It's 100% free!

   This learning path consists of multiple courses that cover everything from the basics of Generative AI to creating your own Image Captioning Models. You'll even learn about responsible AI practices along the way. These courses intend to touch on major topics such as distinctions between AI and machine learning, an Introduction to Google's ML Platform Vertex AI, and the ethical considerations surrounding responsible AI.

2. ChatGPT Prompt Engineering for Developers

   Andrew Ng, partnered with OpenAI to release a practical course using Python and OpenAI API(with the API Key) to understand Prompt Engineering**,** ChatGPT prompt engineering course for developers. This free course offers high-quality content and provides in-depth knowledge of the guidelines for crafting effective prompts.

   This course covers topics such as the Importance of Effective Prompts, How to craft High-Quality Prompts, and Model Limitations(Hallucinations for eg.). This course aims to display how using only one method and an API provided by ChatGPT, one can perform a wide range of NLP tasks such as summarization, translation, sentiment analysis, topic modeling, question-answering, chatbot, and formatting … without the need to create your own model by gathering, preparing your data, and training, testing, and fine-tuning the parameters of your model(s) (and all the time and the skills it requires to perform all of that…).

3. LangChain for LLM Application Development

   LangChain is an innovative and comprehensive open-source framework specifically designed for the development of applications utilizing Large Language Models (LLMs). This powerful framework enables developers to significantly enhance the capabilities of LLMs by seamlessly chaining together multiple components, such as LLMs themselves, intelligent agents, memory systems, indexing mechanisms, and customizable prompt templates.

   Numerous developers have already begun to harness the potential of LangChain to create cutting-edge LLM-based applications.

   One of the most exciting aspects of this course is that it was co-created and presented by none other than Harrison Chase, the visionary mind behind the LangChain framework. Collaborating with renowned AI expert Andrew NG, they provide a crystal-clear explanation of the underlying concept behind LangChain and walk students through a variety of practical examples to demonstrate its real-world applications.

   Upon completing this comprehensive course, you will have gained a thorough understanding of the main modules that serve as the essential building blocks of chains within the LangChain framework. Furthermore, you will be equipped with the skills and knowledge necessary to create intricate chains using these modules, empowering you to develop advanced LLM-based applications with ease and efficiency.

4. Building Systems with the ChatGPT API

   Created by OpenAI and DeepLearning.AI, this course offers great material for learning best practices for building complex applications with LLMs.

   The focus is on creating an entire system (a customer service assistant), not just querying ChatGPT with a single prompt.

   It starts off with an introduction to language models and the important things to know about them such as tokens and chat formats.

   You’ll also learn about very important concepts in working with LLMs such as chain of thought (CoT) reasoning and chaining prompts.

   The evaluation of the output of an LLM is not a straightforward process as there is not only one correct answer in many cases. This course also covers techniques and best practices for evaluating model outputs.

5. How Diffusion Models Work ?

   Developed collaboratively by OpenAI and DeepLearning.AI, this comprehensive course is designed to provide an in-depth understanding of the fundamental principles behind AI-generated artwork and the cutting-edge technology that drives it. The recent surge in the field of AI generative art can be largely attributed to the emergence and growing prominence of the Diffusion Model. This course delves into key topics that are essential for grasping the intricacies of this model and its applications in the world of AI art.

   The curriculum covers a wide range of subjects, including the intuitive reasoning behind the architectural design of Diffusion Models, the process of training these models to generate high-quality art, and the techniques involved in sampling from the model to create diverse and unique AI-generated artworks. By exploring these critical aspects, the course aims to equip learners with the knowledge and skills required to harness the power of AI and Diffusion Models in creating captivating and innovative art pieces.

6. Prompt Engineering for ChatGPT

   This course is offered by Coursera with the aim to teach you how to be an expert user of these generative AI tools. The course shows amazing examples of how you can tap into these generative AI tools' emergent intelligence and reasoning, how you can use them to be more productive day to day, and give you insight into how they work. The course is beginner friendly and easy to follow along!

7. Intro to ChatGPT

   This comprehensive course, offered by Codecademy, delivers an engaging and interactive learning experience focused on ChatGPT, a cutting-edge generative AI system. The curriculum is meticulously designed, incorporating lessons and quizzes to assess participants' comprehension. Key topics covered in the course encompass the ethical considerations and risks associated with AI, practical applications of ChatGPT in daily life and business, as well as other fundamental concepts in the generative AI domain. The course is tailored to be accessible to beginners and easy to follow, ensuring a smooth learning journey for all.

8. Building LLM-Powered Apps

   This comprehensive course, offered by the renowned Weights & Biases, provides an in-depth exploration of building Large Language Model (LLM)-powered applications. This course delves into the intricacies of creating LLM-powered applications using state-of-the-art tools and technologies, such as LLM APIs, Langchain, and W&B Prompts. Throughout the course, you will be guided step-by-step through the entire process of designing, experimenting with, and evaluating LLM-based apps.

   The curriculum covers a wide range of topics, including an introduction to LLMs, understanding the ethical implications of AI, and exploring the practical applications of ChatGPT in various industries. You will also learn how to effectively utilize LLM APIs, Langchain, and W&B Prompts to create powerful, AI-driven applications that can transform daily life and business operations.

   By the end of this course, you will have gained a solid understanding of the generative AI domain, as well as the skills and knowledge required to design, experiment with, and evaluate LLM-powered applications. Whether you are a complete beginner or have some experience in the field, this course will provide you with valuable insights and hands-on experience to help you excel in the rapidly evolving world of AI and LLMs.

9. Training and Fine-tuning Large Language Models (LLMs)

   This course by Weights & Biases is yet to be released.

10. LLM Bootcamp - Spring 2023

    The objective of this course is to facilitate your proficiency in the realm of Large Language Models (LLMs) and Artificial Intelligence (AI), irrespective of your existing expertise. The aim is to ensure you remain current with the latest developments and are prepared to develop remarkable LLM applications.

    Here is a preview of the topics covered in this course: - Comprehensive insights for effective prompting, presented in an accessible manner - Valuable techniques and strategies for improved prompting, including thought decomposition, self-assessment, and idea synthesis - Identifying and circumventing common obstacles, such as "few-shot learning" and tokenization challenges - Implementing test-driven development and continuous integration for LLMs to streamline projects - Emerging trends in user experience design for language user interfaces (LUIs) - Augmenting language model inputs with supplementary knowledge from external sources - Establishing a solid foundation in LLMs - Fundamental Python project tools, data processing, deployment on Modal, and monitoring with Gantry

11. Practical Deep Learning for Coders

    This free course is designed for people with some coding experience who want to learn how to apply deep learning and machine learning to practical problems. Practical Deep Learning for Coders 2022 part 1, recorded at the University of Queensland, covers topics such as how to:

    • Build and train deep learning models for computer vision, natural language processing, tabular analysis, and collaborative filtering problems

    • Deploy models

    • Use PyTorch, the world’s fastest-growing deep learning software, plus popular libraries like fast.ai and Hugging Face

    • Stable Diffusion or Diffusion Models from scratch

12. LangChain AI Handbook

    At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more. The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs. This course by Pinecone and instructor James Briggs, dives into each of the major components of the framework in much more detail starting from the basics behind prompt templates and LLMs. It also explores two LLM options available from the library, using models from Hugging Face Hub or OpenAI.

13. LLM University by Cohere

    A few weeks back, Cohere published a very solid introduction to Large Language Models (LLMs) through their course "LLM University". This course is recommended to people who want a more solid understanding of what the underpinnings of things like ChatGPT look like. The course provides a set of comprehensive learning resources for anyone interested in natural language processing (NLP), from the fundamentals of LLMs all the way to the most advanced topics, including generative AI. LLMU’s comprehensive curriculum aims to give you a rock-solid foundation in Language AI, equipping you with the skills needed to develop your own applications. Whether you want to learn semantic search, generation, classification, embeddings, or any other NLP technique, this is the place for you! The course caters to learners from all backgrounds, and lessons are geared toward anyone excited about language processing: ML beginners, any enthusiast looking to build apps with language AI, and learners who are ready to put their skills into practice. LLM University takes a one-size-fits-all approach, but learners can pick their own path.

14. Generative Model Lecture Series by Stanford University

    "Generative Models" is a lecture series offered by Stanford University on YouTube, providing a comprehensive introduction to generative models. Led by Stanford professors, this course covers both classical and deep learning-based perspectives on generative modeling. Topics include graphical models, variational autoencoders, generative adversarial networks (GANs), and deep autoregressive models. Lectures explore conceptual frameworks and provide insights into practical applications such as image generation, text generation, and more.

15. LangChain & Vector Databases in Production

    Gen AI 360's Foundational Model Certification, a collaborative effort by Activeloop, Towards AI, and Intel Disruptor Initiative, is a must-have for anyone looking to master Large Language Models (LLMs) and incorporate them into AI products. This cutting-edge three-course series equips you with the knowledge and skills to train, fine-tune, and utilize LLMs effectively. With 50+ lessons and 10+ practical projects, you'll learn to leverage LangChain, a robust framework for LLM applications, and explore Deep Lake, a groundbreaking vector database for AI data. This certification is not just about learning, it's about applying these skills in real-world scenarios, making it an invaluable tool for those looking to incorporate LLMs into their organization's AI products. In a nutshell, the Gen AI 360 Foundational Model Certification is a top-notch, free resource that empowers you to harness the power of generative AI.

    Don't miss out on this opportunity to stay ahead of the curve in the rapidly

Additional Resources:

Best LLM and LLMOps Resources for 2023

Final Thoughts

These courses will not make you an LLM expert but they will guide you towards an efficient learning path. They are well-structured and created by experts in the field so any minute you spend on these courses is worth it.

Personally, I believe, Creativity will be the limit in this new world of Generative AI with the growing offers of LLMs and their empires of volumes of data, processing, and knowledge to be generated with each new version.

As Large Language Models (LLMs) continue to evolve, they have already demonstrated remarkable achievements in various domains. For instance, LLMs have been employed to produce authentic and captivating dialogues for chatbots, facilitate language translations with near-human precision, and compose inventive content such as poetry, code, and scripts.

With the ongoing advancement of LLMs, their influence on our lives is expected to expand significantly. Potential applications include enhancing customer service quality, automating tasks within professional settings, and introducing novel forms of entertainment and education.

Nonetheless, it is crucial to acknowledge that LLMs possess certain limitations. They may occasionally generate inaccurate or deceptive information and exhibit biases in their responses. Consequently, it is essential to remain cognizant of these shortcomings and exercise caution when utilizing LLMs.

Thank you for reading! Please reach out to me if you have any feedback or wanna talk about GenAI space in general.

Adios !👋