Why enroll for Large Language Models (LLMs) 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.
Large Language Models 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 Models (LLMs) 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 Models (LLMs) Course with Generative AI
Large Language Models Skills Covered
Leveraging Generative AI
Large Language Models
Prompt Engineering
LLM Application Development
Evaluating Generative Models
Generative AI on Cloud
Large Language Models Generative AI Tools Covered
Large Language Models Course with Generative AI Curriculum
Curriculum Designed by Experts
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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
Hands-on Project
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
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
Hands-on Projects
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
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
Hands-on Projects
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
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
Hands-on Projects
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
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
Hands-on Projects
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 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
Hands-on Projects
Fine-tuning Generative Models using Various Techniques
Evaluating Generative Models
Skills 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
Hands-on Projects
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
Free Career Counselling
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Large Language Models Course with Generative AI Details
About Large Language Models Course with Generative AI
This course on Large Language Models (LLMs) and Generative AI 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 Generative AI 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 and Generative AI in a variety of industrial scenarios.
What are the learning outcomes of this LLMs with Gen AI Course?
Upon completing this LLMs with Gen AI Course, participants will attain the following learning outcomes:
Acquire a profound understanding of Generative AI and 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 LLMs and Generative AI 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 Online Large Language Models Course with Generative AI?
Enroll in the Online Large Language Models Course featuring Generative AI 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 LLM with Generative AI Course?
Individuals with an 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 LLMs with Generative AI 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 and Generative AI 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 Models Course with Generative AI?
This course on Large Language Models with Generative AI 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 Models Course with Generative AI?
The system requirements for this Large Language Models Course with Generative AI 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 Models 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.
Large Language Models Course with Generative AI 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 Models with Generative AI Certification
To unlock Edureka’s Large Language Models (LLMs) Course with Generative AI completion certificate, you must ensure the following:
Completely participate in this Large Language Models (LLMs) Course with Generative AI.
Evaluation and completion of the quizzes and projects listed.
Being a certified LLM with Generative AI 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 LLMs with Generative AI Certification, 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
Your Name
Title
with Grade X
Sample IDNASignature
The Certificate ID can be verified at www.edureka.co/verify to check the authenticity of this certificate
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Large Language Models Course FAQs
What is Generative AI?
Generative AI is a branch of artificial intelligence that focuses on producing or creating fresh content, such as images, text, audio, or even videos, that closely resembles data generated by humans. In contrast to conventional AI models that learn to identify patterns or make decisions based on existing data (discriminative models), generative AI models are trained to produce new data that mirrors the original training data. These models have a wide range of applications, including generating images, and text, synthesizing data, and even engaging in creative tasks like art and music generation.
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 Models 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 Large Language Models Course with Generative AI?
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 Models 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 of Large Language Models Course with Generative AI?
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.