Large Language Models (LLM) Tutorial

60 Lessons

40 hours

Large Language Models (LLM) Tutorial comprehensive tutorial curriculum with practical examples, code snippets, and structured modules.

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About Course

Large Language Models (LLM) Tutorial comprehensive tutorial curriculum with practical examples, code snippets, and structured modules.

Course Curriculum

Course Content

Cluster 1: LLM Fundamentals Getting Started

What Is a Large Language Model (LLM)?
Introduction to Large Language Models
How Large Language Models Work
LLM Tokens, Tokenization and Context Windows
Transformer Architecture Explained
LLM Pretraining, Instruction Tuning and Alignment
LLM Use Cases, Strengths and Limitations
Set Up an LLM Development Environment

Cluster 2: LLM Models Core Concepts

LLM Model Families and Model Selection
Open-Weight vs Closed-Weight LLMs
LLM Parameters, Model Size and Capability
Embeddings and Semantic Representations for LLMs
Attention Mechanisms and Context Processing
LLM Inference, Decoding and Text Generation
Temperature, Top-p and Sampling Strategies
Context Length, Memory and Long-Context Behavior
Multimodal LLMs: Text, Image, Audio and Video
LLM Benchmarks, Leaderboards and Evaluation Sets

Cluster 3: LLM Prompting Interaction

Cluster 4: LLM Applications Orchestration

Cluster 5: LLM Data Embeddings Fine Tuning

Cluster 6: LLM Serving Performance Deployment

Cluster 7: LLM Security Governance Enterprise

Cluster 8: LLM Advanced Topics Career

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₹15,000.00

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Duration: 40 hrs
60 Lessons & Practical Labs
Lifetime Full Access & Free Upgrades
Downloadable Study Materials & Code Labs
Recognized Certification of Completion
24x7 Online Support & Learner Forum

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The trainers explained concepts through real-world attack scenarios, which I was able to apply on the job right after the course. Structured curriculum and hands-on labs were the best part.

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Large Language Models (LLM) Tutorial

About Course

Course Curriculum

Course Content

Cluster 1: LLM Fundamentals Getting Started

What Is a Large Language Model (LLM)?

Introduction to Large Language Models

How Large Language Models Work

LLM Tokens, Tokenization and Context Windows

Transformer Architecture Explained

LLM Pretraining, Instruction Tuning and Alignment

LLM Use Cases, Strengths and Limitations

Set Up an LLM Development Environment

Cluster 2: LLM Models Core Concepts

LLM Model Families and Model Selection

Open-Weight vs Closed-Weight LLMs

LLM Parameters, Model Size and Capability

Embeddings and Semantic Representations for LLMs

Attention Mechanisms and Context Processing

LLM Inference, Decoding and Text Generation

Temperature, Top-p and Sampling Strategies

Context Length, Memory and Long-Context Behavior

Multimodal LLMs: Text, Image, Audio and Video

LLM Benchmarks, Leaderboards and Evaluation Sets

Cluster 3: LLM Prompting Interaction

Prompt Engineering Fundamentals

System Prompts and Instruction Hierarchies

Few-Shot Prompting and In-Context Examples

Structured Outputs and JSON Schemas for LLMs

LLM Tool Calling and Function Calling

Reasoning Models, Explanations and Validation

Prompt Injection and Robust Prompt Design

Prompt Testing, Versioning and Optimization

Cluster 4: LLM Applications Orchestration

Building LLM Applications with APIs

Conversation State and Context Management

LLM Agents, Tools and Workflow Orchestration

LLM Frameworks and Model Abstraction Layers

Streaming LLM Responses and User Experience

Multimodal LLM Application Patterns

Human-in-the-Loop LLM Workflows

LLM Application Architecture Best Practices

Cluster 5: LLM Data Embeddings Fine Tuning

Preparing Data for LLM Applications

Synthetic Data for LLMs: Generation and Quality

LLM Fine-Tuning vs RAG vs Prompting

Parameter-Efficient Fine-Tuning and LoRA

Supervised Fine-Tuning and Dataset Design

Preference Optimization and LLM Alignment Methods

LLM Quantization and Model Compression

LLM Adaptation, Distillation and Evaluation

Cluster 6: LLM Serving Performance Deployment

Running LLMs Locally and Self-Hosted Inference

LLM Inference Servers and Serving Engines

GPU Memory, Batching and LLM Throughput

LLM Latency, Concurrency and Autoscaling

LLM Caching and Cost Optimization

LLM Evaluation Pipelines and Regression Testing

Deploying LLM APIs to Production

LLM Observability, Reliability and Incident Response

Cluster 7: LLM Security Governance Enterprise

LLM Security Best Practices

LLM Privacy, Data Governance and Retention

Responsible AI, Bias and Safety Evaluation

LLM Red Teaming and Adversarial Testing

LLM Policy, Compliance and Auditability

Enterprise LLM Architecture and Governance

Cluster 8: LLM Advanced Topics Career

Production LLM Architecture Patterns

LLMOps and Model Lifecycle Management

LLM Projects, Portfolio and Interview Preparation

LLM Career Path, Skills and Learning Roadmap

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Large Language Models (LLM) Tutorial Certification Exam

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