Home 7 Best + Free RAG Courses & Classes

    7 Best + Free RAG Courses & Classes

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    While selecting the Best RAG Courses and Classes, our team considered certain factors. These were course content comprehensiveness, instructor expertise in RAG, and positive learner outcomes. We gave priority to courses that offered certificates of completion. Following such an approach made sure our recommendations were authoritative.

    Key Takeaways

    • Access to Best RAG Courses.
    • A chance to learn from highly experienced instructors.
    • Certificates available to validate your skills.
    # Course Name University/Organization Ratings Duration
    1. What is Retrieval-Augmented Generation (RAG)? YouTube
    2. Introduction to Retrieval Augmented Generation (RAG) Duke University ★★★★ 3.8 02 Hours
    3. Building Multimodal Search and RAG DeepLearning.AI 01 Hour
    4. Open-source LLMs: Uncensored & secure AI locally with RAG Udemy ★★★★★ 4.8 10 Hours
    5. Knowledge Graphs for RAG DeepLearning.AI ★★★★★ 4.7 01 Hour
    6. Building Agentic RAG with LlamaIndex DeepLearning.AI 01 Hour
    7. JavaScript RAG Web Apps with LlamaIndex DeepLearning.AI 01 Hour
    In order to help our readers in taking a knowledgeable learning decision, TakeThisCourse.net has introduced a metric to measure the effectiveness of an online course. Learn more about how we measure an online course effectiveness.

    Best + Free RAG Courses & Classes….

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    What is Retrieval-Augmented Generation (RAG)?

        • via YouTube

    In this video, the instructor explains all about the LLM/RAG framework. You will understand how the combination of these two can bring such big advantages.

    • The reason why we chose this video is its focus on explaining all about the Large Language Models. You will understand the role of RAG in detail as well.
    • This video is for those who wish to understand how the model gets its info, lending more credibility to what it generates.

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    Coursera Plus Courses

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    Introduction to Retrieval Augmented Generation (RAG)

        • Duke University via Coursera
        • 02 Hours of effort required!
        • ★★★★★ (11 Ratings)

    Introduction to Retrieval Augmented Generation (RAG)

    Online Course Effectiveness Score 
    Content Engagement Practice Career Benefit
    Excellent
    ★★★★★
    Excellent
    ★★★★★
    Fair
    ★★★☆☆
    Fair
    ★★★☆☆

    In this intermediate-level course, you will be taught how to import data into Pandas. You will learn to create embeddings with SentenceTransformers.

    • The best thing about this course is its focus on explaining how to build a retrieval augmented generation (RAG) system with your data.
    • This course is for those who wish to learn to build an end-to-end RAG system with their own data using open-source tools for a powerful generative AI application.

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    I can say this course offers great introduction of RAG. The instructor also shed light on topics like LLM and VectorDB. I learned many new things thanks to this course. (Johnson C)

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    Building Multimodal Search and RAG

        • DeepLearning.AI via Coursera
        • 01 Hour of effort required!
        • Course type (Self-paced)

    Building Multimodal Search and RAG

    An intermediate-level course where you will learn multi modality with constructive learning. You will then learn to create modality-independent embedding for seamless any-to-any retrieval.

    • The reason why we chose this course is its focus on explaining how to build multi modal RAG systems that can retrieve multi modal context. You will learn to reason over it to generate more relevant answers.
    • This course is for those who wish to understand how to implement industry applications of multi modal search and build multi-vector recommended systems.

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    Continue with more Free RAG Courses & Classes…

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    Open-source LLMs: Uncensored & secure AI locally with RAG

        • Arnold Oberleiter via Udemy
        • 1,591+ already enrolled!
        • ★★★★★ (145 Ratings)

    Open-source LLMs: Uncensored & secure AI locally with RAG

    Online Course Effectiveness Score 
    Content Engagement Practice Career Benefit
    Excellent
    ★★★★★
    Excellent
    ★★★★★
    Fair
    ★★★☆☆
    Fair
    ★★★☆☆

    This is a bestseller course where you will understand the differences, advantages, and disadvantages of both open-source and closed-source LLMS. You will learn what LLMs are including ChatGPT, Llama, Phi3, and more.

    • The best thing about this course is its focus on explaining how to find which LLMs are available and which ones are the most suitable to use. You will learn to find the best LLMs in a step-by-step guide.
    • This course is for those who wish to learn all about censored vs uncensored LLMs and understand the requirements for using open-source LLMs locally.

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    This is one of the best courses I ever got to enroll in. I believe all the information in this course is totally updated and helps you understand how to run LLM’s locally and free. The instructor explained in detail how to tune LLM’s and understand what retrieval augmented generation is. I can say this course is a complete package and too good to be missed. (Ken R)

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    Knowledge Graphs for RAG

        • DeepLearning.AI via Coursera
        • 01 Hour of effort required!
        • ★★★★★ (12 Ratings)

    Knowledge Graphs for RAG

    Online Course Effectiveness Score 
    Content Engagement Practice Career Benefit
    Excellent
    ★★★★★
    Excellent
    ★★★★★
    Good
    ★★★★☆
    Good
    ★★★★☆

    Are you interested in learning how to use Neo4j’s query language Cypher to not only manage but retrieve data that is stored in knowledge sports? If yes then this course is the one to take.

    • The best thing about this course is its focus on explaining how to write knowledge graph queries that can find and format text data to provide more relevant context to LLMs for Retrieval Augmented Generation.
    • This course is for those who wish to understand how to build a question-answering system using Neo4j and LangChain to chat with a knowledge graph of structured text documents.

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    This is a very interesting course that offers a detailed overview of how to create a KG using Neo4j. The instructor also explained in detail how to integrate it with LangChain for RAG. I am glad to have found this course and will recommend it to all my friends. (Agrover 112, ★★★★☆)

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    Building Agentic RAG with LlamaIndex

        • DeepLearning.AI via Coursera
        • 01 Hour of effort required!
        • Course type (Self-Paced)

    Building Agentic RAG with LlamaIndex

    Online Course Effectiveness Score 
    Content Engagement Practice Career Benefit
    Excellent
    ★★★★★
    Excellent
    ★★★★★
    Good
    ★★★★☆
    Fair
    ★★★☆☆

    In this highly engaging course, you will learn to build an agent that has the ability to reason over your documents and even answer complex questions.

    • The reason why we chose this course is its focus on explaining how to build a router agent that can help us with Q&A and other summarization tasks. You will also learn to extend that agent and pass all arguments to that agent.
    • This course is for those who wish to learn to design a research agent that can handle multi-documents and learn many ways to debug and control that agent.

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    JavaScript RAG Web Apps with LlamaIndex

        • DeepLearning.AI via Coursera
        • 01 Hour of effort required!
        • Course type (Self-paced)

    JavaScript RAG Web Apps with LlamaIndex

    This is a very interesting course where you will learn to build a RAG application in JavaScript. You will learn to use an intelligent agent that not only discerns but selects from data sources to answer our queries.

    • The best thing about this course is its focus on explaining how to build a full-stack web application with an interactive frontend component and that can interact and chat with your data. You will also understand the right way to enable data chatting.
    • This course is for those who wish to discover all about persisting data and understand streaming responses with the create-llama command-line tool.

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