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Large Language Models (LLMs) Certification Course (2024)

Large Language Models (LLMs) Certification Course (2024)

Name: Large Language Models (LLMs) Certification Course (2024)
Item: Large Language Models (LLMs) Certification Course (2024)
Rating: 5
Author: Sujit Samal

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Edureka's Large Language Model Course is designed by industry experts to help you prepare Machine Learning Engineers, AI Researchers, NLP Specialists, etc. The objective of this course is to help learners improve their programming skills and pursue Certification and Advanced degrees. This LLM course will help you understand various concepts like Transformer Architecture, Prompt Engineering Principles, etc.

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Why should you take Large Language Model Course with Generative AI?

The global generative AI market is projected to grow from $67.18B in 2024 to $967.65B by 2032, at a CAGR of 39.6%.

Bloomberg Intelligence reported that the market size for Generative AI is poised to reach $1.3 Trillion by 2032.

The average annual pay for a Generative AI Engineer in the US is $115,864 a year with a $5,500 cash bonus per year.

Instructor-led Large Language Model Course with Generative AI live online classes

May 30 ^th

Fri & Sat (3.5 Weeks) Weekend Batch

Timings - 09:30 PM to 10:30 PM (EDT) ORIENTATION CLASS

Course Price at

269 299

Can’t find a batch you were looking for?

Curriculum

Generative AI and LLM Essentials

Topics:

Understanding Generative AI
Applications of Generative AI
Types of Generative Models: GANs, VAEs, Autoregressive models
Introduction to Large Language Models (LLMs)
Transformer Architecture
Exploring different Opensource LLMs
Introduction to HuggingFace and its Pre-trained LLM Models
Limitations of LLMs
Advantages and Disadvantages of Different LLM Architectures
Accessing and Using Open-Source LLMs for Projects
Responsible AI Development Practices

Hands-on:

Exploring and Analyzing Different opensource LLMs
Utilizing HuggingFace Pre-trained LLM Models
Implementing Responsible AI Development Practices

Skills You will Learn:

Exploring Different LLMs
Utilizing HuggingFace
Implementing AI Best Practices

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Prompting Techniques for Generative Models

Topics:

Prompt Engineering Principles
What is Prompt Engineering?
Concept and relevance of prompt engineering in generative AI models.
Explore commonly used tools for prompt engineering
Prompt Design Strategies
Types of Prompting
Approaches for writing effective prompts
Best practices for creating impactful prompts
Parameter Tuning

Hands-on:

Designing Precise Prompts
Experimenting with Various Prompt Design Strategies
Advanced Parameter Tuning for Prompt Engineering

Skills You will Learn:

Prompt Engineering Principles
Crafting Effective Prompts
Advanced Parameter Tuning for Prompts

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Interacting with Data using Retrieval-Augmented Generation

Topics:

Understanding RAG
RAG Architecture
Retriever Techniques
Keyword Matching
Sentence Transformers
LLM Integration with Prompt and Retrieved Information
Augmentation Strategies
Benefits of RAG
Access to Real-time Information
Improved Grounding and Factual Accuracy
Advanced RAG: Moving Beyond Naive RAG
Modular RAG
Retrieval Quality Enhancement

Hands-on:

Implementing Retriever Techniques
Integrating LLMs with Retrieved Information for Generation
Experimenting with Augmentation strategies for Retrieval-augmented Generation

Skills You will Learn:

Understanding RAG Architecture
Applying Retriever Techniques
Enhancing Retrieval Quality

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LLMs for Word Embedding and Chunking Mechanism

Topics:

Word Embedding Introduction
Word Embedding Techniques
Capturing Word Relationships
Sentence Embedding Techniques
Introduction to Vector Databases
Different Types of Vector Databases
Chunking
Perform Chunking of the Document
Traditional Chunking mechanism
Advanced Chunking Mechanism
Character Splitting
Recursive Character splitting
Document-based Chunking
Semantic Chunking
Agentic Chunking

Hands-on:

Exploring Different Word Embedding Techniques
Working with Vector Databases forManaging Embeddings
Performing Chunking of Documents using Traditional and Advanced Mechanisms

Skills You will Learn:

Implementing Embedding techniques
Utilizing Vector Databases
Implementing Document Chunking

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LangChain and LlamaIndex for LLM Application Development

Topics:

LangChain Framework
Chaining LLMs with other AI Components for Complex Workflows
Building Applications with Combined Functionalities
LlamaIndex for Large-Scale Factual Knowledge Indexing for LLMs

Hands-on:

Implementing LangChain Framework for Chaining LLMs with other AI Components
Building Applications with Combined Functionalities using LangChain
Utilizing LlamaIndex for Large-scale Factual knowledge indexing for LLMs

Skills:

Implementing LangChain Framework
Integrating AI Components
Indexing Factual Knowledge

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Fine Tuning and Evaluating Generative Models

Topics:

Fine-Tuning Fundamentals
Fine-Tuning Techniques for Generative Models
Data augmentation
Hyperparameter Tuning
Curriculum Learning
Transfer Learning PEFT (Parameter-Efficient Fine-Tuning)
Low-Rank Adaptation (LoRA)
Quantized LoRA (QLoRA)
Feature Extraction
Full Fine-Tuning
Selective Fine-Tuning
Multi-Task Learning
Reinforcement Learning from Human Feedback
Challenges of Evaluating Generative Models
Inception Score
BLEU score
ROUGE score
Qualitative Evaluation

Hands-on:

Fine-tuning Generative Models using Various Techniques
Evaluating Generative Models

Skills You will Learn:

Fine-tuning techniques for Generative Models
Evaluating model performance

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Generative AI on Cloud (Self-paced)

Topics:

Cloud Computing Foundations
AWS S3
Amazon EC2 Trn1n
Amazon EC2 Inf2
Amazon Sagemaker
Amazon Bedrock
Azure OpenAI

Hands-on:

Setting up a Cloud Computing Environment
Navigating Cloud Platforms for AI Development

Skills You will Learn:

Working with Amazon Bedrock
Implementing Azure OpenAI for Generative AI Solutions

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Large Language Model Certification Course Details

What is a Large Language Models Course?

This Large Language Model course (LLMs) covers a wide array of topics ranging from fundamentals of Generative AI and LLMs to advanced methodologies and cloud deployment to equip learners with specialized expertise and practical competencies in this state-of-the-art AI technology. This course delivers a thorough insight into the applications of LLM applications and practical sessions enable participants to engage with different open-source LLMs, apply prompt engineering techniques, and manipulate data using retrieval-augmented generation. Also, students explore word embedding strategies, chunking mechanisms, and software development utilizing frameworks such as LangChain and LlamaIndex. By the end of this program, learners will have the knowledge required to effectively apply LLMs in a variety of industrial scenarios.

What are the learning outcomes of this LLM Online Training Course?

Upon completing this LLM AI Course, participants will attain the following learning outcomes:

Acquire a profound understanding of LLMs, encompassing their fundamental principles, structures, and utilization across diverse fields.
Illustrate the capacity to grasp and scrutinize various categories of generative models like GANs, VAEs, and autoregressive models, alongside elucidating the benefits and constraints of LLM architectures.
Employ expertise in LLM methodologies to investigate and assess publicly available LLMs, execute prompt engineering approaches, and merge LLMs with extracted data for generative assignments.
Examine the efficacy of distinct word embedding methodologies, chunking mechanisms, and retrieval-augmented generation tactics in practical scenarios through interactive activities and projects.
Integrate the comprehension of sophisticated LLM frameworks such as LangChain and LlamaIndex to construct applications with amalgamated functionalities, exploiting AI elements for intricate workflows.
Assess and refine generative models utilizing diverse methods like fine-tuning, hyperparameter optimization, and reinforcement learning, while also appraising model performance through quantitative and qualitative assessment criteria.

Why take up this LLM Course?

Enroll in the Large Language Model Course to attain state-of-the-art proficiency in the domain, enhance your career opportunities within AI-related domains, remain updated on industry developments, and cultivate hands-on competencies related to industry-relevant projects.

Who should take up this Large Language Model Course with Generative AI?

Individuals with interest in enhancing their knowledge and skills in artificial intelligence (AI), specifically in the natural language processing (NLP) domain, are encouraged to enroll in the Large Language Model Course. This course is suitable for professionals who aim to specialize in text creation, language comprehension, and the production of AI-powered content. Furthermore, data scientists, machine learning engineers, AI scholars, and software developers looking to enhance their proficiency in LLMs are encouraged to register. This course is tailored for learners who are keen to delve into state-of-the-art AI technologies, create practical applications, and make contributions to the expanding domain of AI-driven content generation and application development.

What are the prerequisites for this Large Language Model Course?

This LLM training course requires a foundational understanding of Python programming, familiarity with machine learning principles, and natural language processing (NLP). Prior knowledge of TensorFlow and a basic understanding of cloud computing tools are desirable.

What are the system requirements for this Large Language Model Course?

The system requirements for this LLM training course include:

A laptop or desktop computer with a minimum of 8 GB of RAM and Intel Core i3 or above processor to run NLP and machine learning models is required.
A stable and high-speed internet connection is necessary for accessing online course materials, videos, and software.

How will I execute the practicals in this Large Language Model Course?

Practicals for this course will be implemented using Python, VS Code, and Jupyter Notebook. Detailed step-by-step installation guides are available on the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.

What is included in this Introduction to LLM Engineering course?

In this large language model course includes lifetime access, 24/7 support and you will learn the skills of Leveraging Generative AI, Prompt Engineering, Large Language Models, etc, and get industry-relevant projects, Quizzes & Assignments.

What are the other AI related courses provided by Edureka?

Edureka offers various AI-related courses such as Artificial Intelligence Certification Course , ChatGpt Course , Machine Learning Certification Training .. These courses provide a powerful foundation for AI and ML certification.

What are the other resources provided by Edureka?

Edureka provides both Blog and Forum to learners to improve their knowledge and skills in various technical fields.

Who should I contact if I am unable to access my Introduction to LLM Engineer Course?

If you are unable to access your Introduction to LLms course. Then you should contact our Edureka customer support team.

How do I enroll in the LLM Engineering course?

There are simple steps to enroll in this LLM certification course:

Check the Course Details
Create an Account
Click Enroll now
Select your Batch and Timing
Complete your Payment
Get a confirmation mail
Start learning

Can I access the course materials after Large Language Models course ends?

Edureka provides lifetime access to the course content. You can come back any time if you want to brush up your knowledge.

Can I enroll in the LLM Engineering course after it has started?

If you missed out on joining our current batch, then you can connect with our experts to get you enrolled in the next batch.

How do I provide feedback on the LLM course?

We take each feedback seriously. So after completing every class, our Edureka support team will contact you for your valuable feedback on the course.

Are you providing corporate training for this Large Language Model Certification Course?

Yes, we are providing corporate training for this LLM Engineering course.

What is the duration of this Introduction to LLMs Course?

The duration of this LLM Engineering course is 21 hours.

What is the cost of this LLM certification course?

The cost of this LLM Engineer course is INR 15,299/-. Use our no-cost EMI option and get started for just ₹5100/month, With no extra amount. Invest in your future now, and pay later!

View course curriculum

Testimonial Reviews

Souvik KunduLearning to code the web, big data & cloud computing. Aspiring Developer

I'm currently enrolled in a lot of courses offered at Edureka, so I'm attending live classes and usi ... Read More

Michael HarkinsSolution Engineer-Open Source Analytics at IBM, former Systems Architect at Hortonworks

The courses are top rate. The best part is live instruction, with playback. You get all the presenta ... Read More

Muralidhar GaddamDevOps Professional, Project/Program Management Corporate Trainer

I got full value from Edureka's DevOps course. I had to come out of my comfort zone as I was expecti ... Read More

Sujit SamalTechnical Lead, Wipro

I have been an Edureka!'s happy customer since 2013. I am a customer of Edureka enrolled for Big dat ... Read More

Anil AlgolePrinciple Consultant at Infosys

Experience with Edureka is world class. I took 2 courses Informatica PowerCenter 9.x and Tableau Cer ... Read More

Suman RajaIT Analyst at Tata Consultancy Services Greater Philadelphia Area Information Technology and Services

Definitely there is no doubt in saying that all the instructors at Edureka are industry experienced ... Read More

Video Reviews

Vinayak shares his Edureka learning experience and how our Big Data training helped him achieve his dream career path.

Our learner Balasubramaniam shares his Edureka learning experience and how our training helped him stay updated with evolving technologies.

Sriram speaks about his learning experience with Edureka and how our Hadoop training helped him execute his Big Data project efficiently.

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Large Language Model Course FAQs

What are Large Language Models?

Large language models are powerful engines that understand and create a language like never before. These smart systems, trained on massive amounts of text, can recognize, translate, predict, or even generate new information.

What is Prompt Engineering?‎

Prompt engineering involves optimizing artificial intelligence engineering for multiple purposes. It includes refining large language models (LLMs) using specific prompts and recommended outputs. Additionally, it focuses on enhancing input to different generative AI services to make text or images. With advancements in generative AI tools, prompt engineering becomes crucial for generating diverse content, such as robotic process automation bots, 3D assets, scripts, robot instructions, and various digital artifacts.

Will I get placement assistance after completing this Large Language Model Course?

To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.

Who are the instructors for the Introduction to Large Language Models Certification Course?

All the instructors at Edureka are practitioners from the industry with a minimum of 10–12 years of relevant experience. They are subject matter experts and are trained by Edureka to provide an awesome learning experience to the participants.

What if I have more queries after the completion of the Large Language Model Course?

Just give us a call at +91 98702 76459/1844 230 6365 (US Tollfree Number) or email at sales@edureka.co.

What if I miss a class on LLM Training?

You will never miss a lecture at Edureka! You can choose either of the two options:

View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.

What is the difference between NLP and LLM?

Both NLP and LLM are related to the AI field. But little differences are there. NLP is used to interact between humans and computers. For example translation, sentiment analysis, and speech recognition come under NLP. LLM is like ChatGPT-4 model. It is used in deep learning to complete the NLP tasks.

Is ChatGPT a large language model?

Yes, ChatGPT is LLM developed by open AI. The architecture of LLM is based on GPT-4. The current version of chatgpt is to generate human-like text content, answering queries. LLM applications are mostly used in educational assistance, chatbots, etc.

Is LLM a good career option?

Yes, LLM is a good career option because AI and ML play a major role in various industries by implementing applications like chatbots, translation, content creation, etc. Industries are providing competitive salaries based on your skills, and experience. Continuous learning ensures your professional growth.

What are the other Courses on Artificial Intelligence provided by Edureka?

Here is the list of courses offered by Edureka:

What is the difference between GPT and LLM?

GPT model is a particular type of LLM. GPT is developed by OpenAI. Based on your prompt GPT will provide relevant content. LLM is designed to perform human tasks like translation, summarization, text completion, etc. All GPT models are related to LLM but all LLM models are not related to GPT.

What is an example of an LLM?

Chatbots and virtual assistants are the best examples of LLM. LLM is used in customer support to help the users. Based on user prompts chatbots are answering questions, generating texts, etc.

What is the architecture of Large Language Models?

Layers like recurrent layers, feedforward layers, embedding layers, and attention layers are the layers in LLM architecture. Based on user prompts these layers will work together to generate the output.

What are the best books can i refer for Large Language Models?

Here are a few Large Language Model books:

Natural Language Processing with Transformers: Building Language Applications with Hugging Face by Lewis Tunstall.
Quick Start Guide to Large Language Models by Sinan Ozdemir.

Is Bert a Large Language Model?

Yes, Bert is a Large Language Model. It is developed by Google. GPT is another best example of LLM.

What is the application of Large Language Models?

In the current world, LLMs are used in applications like language translation, sentence completion, sentiment analysis, question answering, mathematical equations, etc. Human errors are avoided by using LLM in these applications.

What companies are developing Large Language Models?

Some top companies like OpenAI, Google, Microsoft, Facebook, IBM, Amazon, and NVIDIA are developing some new innovative ideas in LLM to implement in various applications.

Suggest some projects for Large Language Models?

PDF-Chat App, Meta Data Generation, Fake News Detection, Question-Answering System, E-commerce Product Search Relevance, Credit Card Fraud Detection, etc. These are few simple projects that will help you improve your practical skills.

What are the benefits of Large Language Models?

Personalize recommendations, improve accessibility, research, creativity, boost efficiency, reducing cost and human errors, etc. There are some benefits of LLM.

View course curriculum

Be future ready. Start learning

Structure your learning and get a certificate to prove it.

Large Language Models (LLMs) Certification Course (2024)

Why should you take Large Language Model Course with Generative AI?

Instructor-led Large Language Model Course with Generative AI live online classes

Financing Options

Curriculum

Generative AI and LLM Essentials

Prompting Techniques for Generative Models

Interacting with Data using Retrieval-Augmented Generation

LLMs for Word Embedding and Chunking Mechanism

LangChain and LlamaIndex for LLM Application Development

Fine Tuning and Evaluating Generative Models

Generative AI on Cloud (Self-paced)

Large Language Model Certification Course Details

What is a Large Language Models Course?

What are the learning outcomes of this LLM Online Training Course?

Why take up this LLM Course?

Who should take up this Large Language Model Course with Generative AI?

What are the prerequisites for this Large Language Model Course?

What are the system requirements for this Large Language Model Course?

How will I execute the practicals in this Large Language Model Course?

What is included in this Introduction to LLM Engineering course?

What are the other AI related courses provided by Edureka?

What are the other resources provided by Edureka?

Who should I contact if I am unable to access my Introduction to LLM Engineer Course?

How do I enroll in the LLM Engineering course?

Can I access the course materials after Large Language Models course ends?

Can I enroll in the LLM Engineering course after it has started?

How do I provide feedback on the LLM course?

Are you providing corporate training for this Large Language Model Certification Course?

What is the duration of this Introduction to LLMs Course?

What is the cost of this LLM certification course?

Testimonial Reviews

Video Reviews

Large Language Model Course FAQs

What are Large Language Models?

What is Prompt Engineering?‎

Will I get placement assistance after completing this Large Language Model Course?

Who are the instructors for the Introduction to Large Language Models Certification Course?

What if I have more queries after the completion of the Large Language Model Course?

What if I miss a class on LLM Training?

What is the difference between NLP and LLM?

Is ChatGPT a large language model?

Is LLM a good career option?

What are the other Courses on Artificial Intelligence provided by Edureka?

What is the difference between GPT and LLM?

What is an example of an LLM?

What is the architecture of Large Language Models?

What are the best books can i refer for Large Language Models?

Is Bert a Large Language Model?

What is the application of Large Language Models?

What companies are developing Large Language Models?

Suggest some projects for Large Language Models?

What are the benefits of Large Language Models?

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