Large Language Model (LLM) Training

WhatsApp +91 93517 82674 · Mon-Sat, 9 AM - 8 PM

Assure Success

Large Language Model (LLM) Training — Course brochure | Assure Success

About the course

Why take Large Language Model (LLM) Training?

The Large Language Model (LLM) Training course gives engineers and data practitioners a thorough, end-to-end understanding of LLMs, from how they are trained to how they are served and used inside products. It sits between a conceptual introduction and a pure application course: you look under the hood far enough to make good engineering decisions, then build.

You study the transformer architecture, tokenisation, pretraining objectives, data curation, scaling behaviour, post-training with instruction tuning and preference optimisation, and the role of mixture-of-experts and long-context techniques. You then work with inference: sampling strategies, quantisation, KV caching, batching and serving open models. Application modules cover prompt and output design, retrieval-augmented generation, tool calling, evaluation, guardrails and cost control. Fine-tuning is introduced and compared with other adaptation options.

There is no external exam. You finish with a capstone and receive an Assure Success course-completion certificate. Pretraining a frontier model from scratch is not practical in a course, so labs use small models and hosted APIs.

Who should attend

● ML engineers, data scientists and software engineers working with LLMs
● AI engineers who want to understand the models they build on
● Technical architects evaluating open and hosted models
● Graduates and researchers targeting LLM-focused roles
● Learners who completed Deep Learning or Generative AI Fundamentals

Prerequisites

● Good Python skills and basic machine learning knowledge
● Familiarity with neural networks; Deep Learning Certification Training is a good preparation
● Comfort with basic linear algebra and probability

What this certification does for you

Depth for good decisions

Knowing how models are trained and served helps you debug and optimise real systems.

Model-agnostic

Principles apply across open and hosted model families.

Cost and performance literacy

You learn to estimate memory, latency and spend before building.

Evaluation mindset

You learn to prove quality rather than assume it.

Curriculum

10 modules, step by step

MODULE 1

Language Modelling Foundations

Understand the objective behind LLMs.

› From n-grams to neural language models
› Next-token prediction
› Perplexity and evaluation basics
› Tokenisation (BPE, SentencePiece)

MODULE 2

The Transformer in Depth

Master the architecture.

› Self-attention and multi-head attention
› Positional encodings including rotary embeddings
› Feed-forward layers, normalisation and residuals
› Build a small GPT-style model in PyTorch

MODULE 3

Pretraining and Data

Understand how base models are made.

› Data collection, filtering and de-duplication
› Pretraining objectives and optimisers
› Scaling laws and compute budgets
› Distributed training concepts

MODULE 4

Post-Training and Alignment

See how base models become assistants.

› Supervised instruction tuning
› Preference-based optimisation
› Reasoning-focused training overview
› Safety training and red teaming

MODULE 5

Architectures and Long Context

Know the design variations.

› Mixture-of-experts
› Grouped-query attention and efficiency tricks
› Long-context methods
› Multimodal extensions

MODULE 6

Inference and Serving

Run models efficiently.

› Sampling strategies
› KV cache, batching and speculative decoding
› Quantisation formats
› Serving engines and GPU sizing

MODULE 7

Building Applications

Create LLM-powered features.

› Prompt and output design
› RAG pipelines
› Function and tool calling
› Conversation state and memory

MODULE 8

Evaluation and Observability

Measure and monitor.

› Benchmarks and their pitfalls
› LLM-as-judge and human review
› Tracing and logging
› Regression test suites

MODULE 9

Adaptation, Safety and Governance

Customise and control.

› LoRA fine-tuning overview
› Prompt injection and jailbreak defences
› Privacy, licensing and copyright issues
› Documentation and risk assessments

MODULE 10

Capstone Project

Deliver an LLM solution with evidence.

› Define the task and metrics
› Build with RAG, tools or tuning
› Evaluate cost, latency and quality
› Present and review

What you get

Why take Large Language Model (LLM) Training?

Who should attend

Prerequisites

What this certification does for you

10 modules, step by step

Certified by Assure Success

Your path to certification

Skills you will gain

Where this course can take you

Why learn with Assure Success