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Large Language Model(LLM)
Learn everything there is to know about Large Language Models (LLMs) and get real-world experience building, fine-tuning, and deploying AI-powered apps. Learn how to use LLMs for chatbots, natural language processing, and business solutions.
5/5(4,890 Reviews)
Level
Advanced
Duration
8 weeks
What is Large Language Model(LLM)?
This course will give you a deep understanding of Large Language Models, including how they are built, how they are trained, and how they can be used in real life. You will learn how to quickly build, fine-tune, and add LLMs to business processes. You'll learn how to use LLMs for text generation, summarization, and conversational AI through hands-on projects and case studies. By the end, you'll know how to use LLM frameworks to make and run smart AI-driven apps.
Training Plan
About trainer
Working professional who is carrying more then 10 years of industry experience.
Decks
The candidate will get access to the presentation taken-up during the session.
Notes
Consolidated notes in word document.
Assignments
Assignments for every module will be covered.
MCQs
MCQs for every module covered in the session would be provided.
Video Recording
Daily Session would be recorded and shared to the candidate.
Dumps for Certification
We will provide dumps for the certification exam, which will help you to prepare for the exam.
Updated Content
We provide Generative AI Driven content By experts.
Projects
3 Live Projects will be provided for practice.
Large Language Model(LLM) Course Curriculum
.
Introduction to LLMs
Section 1
Understand the fundamentals of Large Language Models and their evolution.
AI & NLP Overview
Evolution of Language Models
Transformer Architecture Basics
Applications of LLMs
Limitations & Challenges
.
Foundations of Natural Language Processing (NLP)
Section 2
Learn the fundamentals of NLP required to understand LLMs. Covers text preprocessing, embeddings, and linguistic concepts.
Tokenization
Stemming & Lemmatization
Word Embeddings
Contextual Representations
.
Neural Networks & Transformers
Section 3
Dive into deep learning foundations behind LLMs. Understand RNNs, attention mechanisms, and the transformer architecture.
RNNs & LSTMs
Attention Mechanism
Transformer Models
Encoder-Decoder
.
Training Large Language Models
Section 4
Explore how LLMs are trained with massive datasets and compute power. Learn pretraining, fine-tuning, and optimization techniques.
Pretraining Process
Fine-tuning Methods
Transfer Learning
Optimization Strategies
.
Popular LLM Architectures
Section 5
Study key LLM architectures like GPT, BERT, T5, and LLaMA. Compare their design, capabilities, and applications.
GPT Family
BERT Models
T5 & Seq2Seq
Open-Source LLMs
.
Prompt Engineering & In-Context Learning
Section 6
Learn how to design effective prompts for LLMs. Explore zero-shot, few-shot learning, and chain-of-thought reasoning.
Prompt Design
Zero-shot Learning
Few-shot Learning
Chain-of-Thought
.
Fine-Tuning & Customization
Section 7
Customize LLMs for domain-specific tasks. Understand instruction tuning, RLHF (Reinforcement Learning with Human Feedback), and adapters.
Instruction Tuning
RLHF Basics
Low-Rank Adaptation (LoRA)
Domain Adaptation
.
Evaluation of LLMs
Section 8
Learn how to measure LLM performance using benchmarks and metrics. Study both quantitative and qualitative evaluation techniques.
Accuracy & BLEU
Perplexity
Human Evaluation
Bias & Fairness
.
LLM Deployment & Scaling
Section 9
Understand how to deploy LLMs efficiently using APIs, cloud platforms, and optimization techniques for inference at scale.
API Integration
Cloud Deployment
Model Compression
Serving at Scale
.
Safety, Ethics & Responsible AI
Section 10
Study the challenges of bias, hallucination, and misinformation in LLMs. Learn guidelines for safe and ethical AI usage.
AI Bias
Hallucination Control
Misinformation Risks
Responsible AI
Key Projects
Large Language Model(LLM)
Intelligent Chatbot using LLMs
Use a pre-trained LLM to make a chatbot for customer service. The chatbot should be able to answer frequently asked questions, give answers that make sense in context, and send problems up the chain of command when necessary. You will use prompt engineering and APIs to deploy your work.
Text Summarization & Sentiment Analysis Tool
Make a tool that can read long documents or reviews and make short summaries while also doing sentiment analysis. The main goal of this project is to improve LLMs for tasks like summarization and classification.
Domain-Specific LLM Fine-tuning
Fine-tune an open-source LLM on datasets that are specific to your field, like healthcare, finance, or education. The project will test your skills in data preprocessing, fine-tuning, and measuring performance improvements.
Large Language Model(LLM) – Associate Training Program
Category
Associate
Exam Name:
Large Language Model(LLM) – Associate
Exam Code:
N.A.
Exam Duration:
N.A.
Exam Format:
N.A.
Passing Score:
N.A.
Our Testimonials
Frequently Asked Questions (FAQs)
What is the prerequisite knowledge required for this course?
It will help if you know the basics of Python, machine learning, and NLP, but beginners can also follow along with help.
What is a Large Language Model (LLM)?
A Large Language Model (LLM) is an AI model trained on massive text datasets to understand, generate, and analyze human language. It powers tools like ChatGPT, GPT-4, and Bard. TechPratham’s training teaches learners how LLMs work and how to apply them in real-world tasks like content creation, research, and automation
How do LLMs work?
LLMs use deep learning, transformers, and natural language processing to predict text and generate coherent responses. They learn context, semantics, and grammar from large datasets. TechPratham explains the architecture, training process, and applications of LLMs in an easy-to-understand way.
What are the applications of LLMs?
LLMs are used for chatbots, content generation, coding assistants, summarization, translation, and question-answering systems. TechPratham provides hands-on exercises to apply LLMs across industries like business, healthcare, and education.
Can LLMs replace humans in writing or research?
No, LLMs assist humans by speeding up tasks and generating ideas but cannot fully replace creativity, critical thinking, and domain expertise. TechPratham teaches how to combine human judgment with LLM outputs for optimal results.
Do I need coding knowledge to work with LLMs?
Basic coding knowledge can help but is not mandatory for using prebuilt LLM tools. TechPratham ensures both technical and non-technical learners can harness LLMs effectively for research, content, and business automation.
How accurate are LLM outputs?
LLM outputs are generally reliable but may occasionally include errors or outdated information. TechPratham trains learners on verification techniques, evaluation, and responsible AI usage to ensure accuracy and trustworthiness.
What skills can I gain from LLM training?
Learners develop prompt engineering, AI content generation, natural language understanding, summarization, and automation skills. TechPratham emphasizes hands-on projects to ensure practical mastery of LLM tools.
How are LLMs used in business?
Businesses use LLMs for customer support, marketing, data analysis, report generation, and workflow automation. TechPratham provides real-world projects demonstrating LLM deployment in enterprise environments.
Are LLMs safe to use?
LLMs are generally safe, but outputs should be monitored to avoid misinformation and bias. TechPratham teaches best practices for ethical and responsible AI deployment, including content verification and compliance.
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