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Large Language Models (LLMs) Training Course
Large Language Models (LLMs) Training Course
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Live Online Classes starting on 28th Sep 2024
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Instructor-led Introduction to LLMs live online Training Schedule
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Why enroll for Large Language Model Course with Generative AI?
Large Language Model Training Course Benefits
Bloomberg recently reported that the generative AI market is poised to reach $1.3 trillion by 2032, and the World Economic Forum's “The Future of Jobs Report 2020" predicts AI will replace 85 million jobs globally by 2025. Our course focusing on Large Language Models (LLMs) with Generative AI is designed to provide learners with a profound understanding of this state-of-the-art AI technology and practical expertise. This course will help the learners to gain mastery over the domain and elevate their careers to greater heights.
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Why Large Language Model Course with Generative AI from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Large Language Model Course with Generative AI
Skills Covered in Large Language Model Course
Tools Covered in Large Language Model Training Course
LLM Online Training Course Curriculum
Curriculum Designed by Experts
Generative AI and LLM Essentials
11 Topics
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
![skill]( "Hands On Experience")
- Exploring and Analyzing Different opensource LLMs
- Utilizing HuggingFace Pre-trained LLM Models
- Implementing Responsible AI Development Practices
![skill]( "skill you will learn")
- Exploring Different LLMs
- Utilizing HuggingFace
- Implementing AI Best Practices
Prompting Techniques for Generative Models
9 Topics
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
![skill]( "Hands On Experience")
- Designing Precise Prompts
- Experimenting with Various Prompt Design Strategies
- Advanced Parameter Tuning for Prompt Engineering
![skill]( "skill you will learn")
- Prompt Engineering Principles
- Crafting Effective Prompts
- Advanced Parameter Tuning for Prompts
Interacting with Data using Retrieval-Augmented Generation
13 Topics
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
![skill]( "Hands On Experience")
- Implementing Retriever Techniques
- Integrating LLMs with Retrieved Information for Generation
- Experimenting with Augmentation strategies for Retrieval-augmented Generation
![skill]( "skill you will learn")
- Understanding RAG Architecture
- Applying Retriever Techniques
- Enhancing Retrieval Quality
LLMs for Word Embedding and Chunking Mechanism
15 Topics
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
![skill]( "Hands On Experience")
- Exploring Different Word Embedding Techniques
- Working with Vector Databases forManaging Embeddings
- Performing Chunking of Documents using Traditional and Advanced Mechanisms
![skill]( "skill you will learn")
- Implementing Embedding techniques
- Utilizing Vector Databases
- Implementing Document Chunking
LangChain and LlamaIndex for LLM Application Development
4 Topics
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
![skill]( "Hands On Experience")
- 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
![skill]( "skill you will learn")
- Implementing LangChain Framework
- Integrating AI Components
- Indexing Factual Knowledge
Fine Tuning and Evaluating Generative Models
20 Topics
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
![skill]( "Hands On Experience")
- Fine-tuning Generative Models using Various Techniques
- Evaluating Generative Models
![skill]( "skill you will learn")
- Fine-tuning techniques for Generative Models
- Evaluating model performance
Generative AI on Cloud (Self-paced)
7 Topics
Topics
- Cloud Computing Foundations
- AWS S3
- Amazon EC2 Trn1n
- Amazon EC2 Inf2
- Amazon Sagemaker
- Amazon Bedrock
- Azure OpenAI
![skill]( "Hands On Experience")
- Setting up a Cloud Computing Environment
- Navigating Cloud Platforms for AI Development
![skill]( "skill 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?
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!
LLM Training Projects
Conversational Chatbot Development with Langchain
Create dynamic conversational experiences with ease by leveraging Langchain's sophisticated framework, transforming the landscape of customer support and interaction through impr....Develop a PDF-Chat Application with LangChain
Create a seamless PDF-chat application using LangChain's advanced capabilities, which allows users to effectively summarize the document content to enhance productivity.Large Language Model Certification Training
To unlock Edureka’s Large Language Models (LLMs) course completion certificate, you must ensure the following:
- Completely participate in this LLM Training Course.
- Evaluation and completion of the quizzes and projects listed.
Being an LLM certified expert offers numerous benefits as it endorses learners’ expertise in this cutting-edge technology. This certification enhances the professional credentials of the learners and opens various new avenues for career development.
After completing the LLM Training Course, you will qualify for various job opportunities. These positions encompass roles such as:
- LLM Developer
- LLM Engineer
- Generative AI Engineer
- Generative AI Analyst
- NLP Engineer
- Machine Learning Engineer
- Data Scientist
- AI Research Scientist
- AI Product Manager
- Generative AI Consultant
John Doe
Title
with Grade X
XYZ123431st Jul 2024
The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate
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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.
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