Retrieval Augmented Generation Tutorials & Courses in 2024

Are you looking for Retrieval Augmented Generation Tutorials & Courses?… If yes, this blog is for you. In this blog, I will share the Best Retrieval Augmented Generation Tutorials & Courses with you.

Now, without further ado, let’s get started-

Retrieval Augmented Generation Tutorials & Courses

First, let’s understand What is Retrieval Augmented Generation (RAG) in AI.

What is Retrieval Augmented Generation (RAG) in AI?

Imagine you’re writing a story, and you want to include some cool facts or information to make it more interesting. But, here’s the catch: you’re not sure what facts to add or where to find them. That’s where RAG comes in.

Think of RAG as having a super-smart assistant who can help you find just the right information you need, exactly when you need it. It’s like having Google, but way smarter.

How Retrieval Augmented Generation (RAG) Works?

1. Generation: First, you start by generating some text, like a question or a statement. For example, you might write, “Tell me about space travel.”
2. Retrieval: Then, RAG springs into action. It goes through a huge database of information, kind of like flipping through a gigantic bookshelf filled with books about everything under the sun. It finds the most relevant information related to your query. So, for our space travel example, RAG might find information about different space missions, astronauts, and how rockets work.
3. Augmentation: Finally, RAG takes all that juicy information it found and adds it to your original text. It’s like your story just got a major upgrade with all these cool facts and details.

So, with RAG, you can create content that’s not only well-written but also packed with accurate and interesting information. It’s like having a knowledgeable friend by your side whenever you’re writing, ready to help you make your work shine. And that’s Retrieval Augmented Generation in a nutshell!

Now, let’s move to the Retrieval-Augmented Generation Tutorials & Courses–

1. Introduction to Retrieval Augmented Generation (RAG)– Guided Project

Time to Complete- 2 hours

Level- Intermediate

In this 2-hour project-based course, you will learn how to import data into Pandas, create embeddings with SentenceTransformers, and build a retrieval augmented generation (RAG) system with your data, Qdrant, and a large language model (LLM) like LlamaFile or OpenAI. The course begins with teaching you how to import and manage data using Pandas, a powerful Python library for data manipulation and analysis.

You will then move on to creating embeddings using SentenceTransformers, which are numerical representations of text that help in understanding the context and meaning of the data. The main focus of the course is to build a RAG system, which combines retrieval techniques with generative AI to enhance the capabilities of language models.

Additionally, you will learn to integrate your data with Qdrant, an open-source vector search engine, and an LLM. This integration allows you to create a more powerful and efficient generative AI application.

By the end of this hands-on course, you will have built an end-to-end RAG system using your own data and open-source tools, equipping you with practical skills to develop advanced AI applications.

Who Should Enroll?

• This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who are looking to enhance their skills and apply cutting-edge techniques in their projects.

Interested in Enroll?

If yes, then check- Introduction to Retrieval Augmented Generation (RAG)

2. Generative Adversarial Networks (GANs) Specialization– Coursera

Time to Complete- 3 months ( If you spend 8 hours per week)

Level- Intermediate

A generative Adversarial Network (GAN) is a powerful algorithm of Deep Learning. Generative Adversarial Network is used in Image Generation, Video Generation, and Audio Generation. In short, GAN is a Robot Artist, who can create any kind of art perfectly.

In this Generative Adversarial Networks (GANs) Specialization, you will learn how to build basic GANs using PyTorch and advanced DCGANs using convolutional layers.

You will use GANs for data augmentation and privacy preservation, survey GANs applications, and examine and build Pix2Pix and CycleGAN for image translation.

There are 3 courses in this Specialization program where you will gain hands-on experience in GANs. Now, let’s see all the 3 courses of this Specialization Program-

Courses Include-

1. Build Basic Generative Adversarial Networks (GANs)
2. Build Better Generative Adversarial Networks (GANs)
3. Apply Generative Adversarial Networks (GANs)

Who Should Enroll?

• Those who have a working knowledge of AI, deep learning, and convolutional neural networks. And have intermediate Python skills plus familiarity with any deep learning framework (TensorFlow, Keras, or PyTorch).
• You should also be proficient in basic calculus, linear algebra, and statistics.

Interested in Enroll?

If yes, then check it out here–Generative Adversarial Networks (GANs) Specialization

3. Large Language Models (LLMs) & Text Generation– Udacity

Time to Complete- 4 weeks

Level- Intermediate

In this comprehensive course, you will learn key topics essential for working with advanced AI models. The course starts with an introduction to Large Language Models (LLMs), where you will understand their different types, strengths, and limitations. You will also learn how to use inference and decoding settings effectively. Next, the course covers the basics of Natural Language Processing (NLP), including text encoding and text generation, which are fundamental for working with LLMs.

You will then dive into transformer architectures, learning about attention mechanisms and other important components that make these models powerful. The course also teaches you how to create a custom Q&A bot using OpenAI, giving you practical experience with advanced language processing tools. Additionally, you will learn how to build custom datasets for fine-tuning LLMs and performing retrieval augmented generation.

The course concludes with a hands-on project where you will apply everything you’ve learned to create your own custom chatbot using a dataset of your choice. By the end of this course, you will have a solid understanding of LLMs, NLP basics, transformer architectures, and custom dataset creation. You will also gain practical experience in building a custom Q&A bot and a chatbot.

Who Should Enroll?

• This course is ideal for data scientists, machine learning engineers, and AI enthusiasts looking to deepen their knowledge and skills in advanced language models and AI applications.

Interested in Enroll?

If yes, then check out all the details here- Large Language Models (LLMs) & Text Generation

4. Building Generative AI Solutions– Udacity

Time to Complete- 4 weeks

Level- Intermediate

In this course, you will learn how to build advanced generative AI applications. You will start with an introduction to building generative apps, where you will understand how to design and implement Generative AI using Large Language Models (LLMs) in various features and solutions.

Next, you will explore vector databases, which are important for improving AI’s long-term memory. This lesson covers the basic concepts, retrieval methods, and advanced indexing techniques used with vector databases.

You will then move on to developing generative AI solutions with LangChain, a framework that helps you work with LLMs and build the next generation of AI applications. This part of the course includes hands-on practice with LangChain.

The course ends with a hands-on project where you will create a personalized real estate agent. In this project, you will use LLMs for generating content and vector databases for searching and providing customized real estate listings.

By the end of this course, you will have a clear understanding of how to build generative AI applications, use vector databases, and work with LangChain. You will also gain practical experience by developing a real estate agent application.

Who Should Enroll?

• This course is suited for developers, data scientists, and AI enthusiasts who understand database basics, intermediate Python, generative AI, and API requests.

Interested in Enroll?

If yes, then check out all the details here- Building Generative AI Solutions

5. OpenAI GPTs: Creating Your Own Custom AI Assistants– Coursera

Time to Complete- 7 hours

Level- Beginner

This course will teach you how to create and deploy custom GPTs tailored to different needs and industries. It covers three key areas: first, you’ll learn how to build GPTs that can use your documents to answer questions and interact with users, while also customizing their tone and style. Second, you’ll discover how to design and implement tests to ensure your GPTs are accurate and effective.

Finally, you will explore real-world applications, such as creating a virtual tutor for personalized education, a Culinary GPT for recipe management, a GPT for travel and business expense management, and a GPT for marketing and advertising campaign management. By the end of the course, you will be equipped to develop GPTs that can improve various fields. This course is ideal if you want to learn how to create advanced AI assistants tailored to specific purposes.

Who Should Enroll?

• Those who are beginners.

Interested in Enroll?

If yes, then check out all the details here- OpenAI GPTs: Creating Your Own Custom AI Assistants

6. Master Retrieval-Augmented Generation (RAG) Systems– Udemy

Time to Complete- 1.5 hours

Level- Intermediate

In this course, you will learn about key aspects of Retrieval-Augmented Generation (RAG) systems. You will begin by understanding the RAG Triad, which consists of the Retriever, Generator, and Fusion Module. Then, you will explore advanced retrieval techniques, including both sparse and dense methods, such as Dense Passage Retrieval (DPR).

The course will also teach you how to generate coherent responses that are fluent and contextually appropriate based on retrieved documents.

Finally, you will learn query expansion and re-ranking techniques to improve the relevance of documents through effective re-ranking strategies. By the end of the course, you will have a clear understanding of RAG systems and advanced methods for enhancing retrieval and response quality.

Who Should Enroll?

• Those who have a basic understanding of AI and NLP Concepts and proficiency in Python.

Interested in Enroll?

If yes, then check out all the details here- Master Retrieval-Augmented Generation (RAG) Systems

7. Large Language Models (LLMs) Concepts– DataCamp

Time to Complete- 2 hours

Level- Intermediate

In this course, you’ll explore the interesting world of Large Language Models (LLMs) and see how they’re changing the field of AI. You’ll find out why LLMs are becoming popular, thanks to things like the deep learning revolution, more data available, and better computers. As you go along, discover how LLMs play a big role in real-life areas like finance and creating content.

You will learn about the important parts of LLMs, like how they understand language and get better with practice. Explore the ways LLMs are trained, including cool techniques like predicting the next word and using attention.

By the end of it all, you’ll have a good understanding of LLMs, what they can do, where they’re used, and what challenges they bring.

Who Should Enroll?

• Those who have a previous understanding of Machine Learning.

Interested in Enroll?

If yes, then check out all the details here- Large Language Models (LLMs) Concepts

8. Operationalizing LLMs on Azure– Duke University

Time to Complete- 10 hours

Level- Intermediate

This course is designed for beginners and intermediate learners, including data scientists, AI enthusiasts, and professionals who want to use Azure for Large Language Models (LLMs). If you have basic programming skills and familiarity with Azure, this course will take you through a four-week journey.

In the first week, you will learn about Azure’s AI services and the Azure portal, including insights into large language models, their functionalities, and risk mitigation strategies. The following weeks cover practical applications, such as using Azure Machine Learning, managing GPU quotas, deploying models, and utilizing the Azure OpenAI Service.

As you progress, the course will teach you about query crafting, Semantic Kernel implementation, and strategies to optimize interactions with LLMs on Azure. In the final week, you will focus on architectural patterns, deployment strategies, and hands-on application building using RAG, Azure services, and GitHub Actions workflows.

By the end of this course, you will have the skills to deploy, optimize, and build large-scale applications using Azure and LLMs.

Who Should Enroll?

• Those who have experience in Microsoft Azure or related Cloud Computing platforms as well as beginner programming in Python.

Interested in Enroll?

If yes, then check out all the details here- Operationalizing LLMs on Azure

Here the list of “Retrieval Augmented Generation Tutorials & Courses” ends. I hope these Retrieval Augmented Generation Tutorials & Courses will definitely help you. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

I tried to cover the 8 Best Retrieval Augmented Generation Tutorials & Courses in this article. If you have any doubts or questions feel free to ask me in the comment section.

All the Best!

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