Professional skills

Transformers and Large Language Models

Build practical skills in tokens attention and transformer architecture and using pre-trained language models.

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THE BIG PICTURE

About this course

Transformers and Large Language Models develops your understanding of tokens attention and transformer architecture. The learning path connects using pre-trained language models with retrieval grounding and evaluation, then brings these ideas together through inference privacy and deployment constraints. Use the course outline to discuss the scope that fits your role, current experience, and team objectives. A suitable training plan should connect each concept to a relevant work scenario, give you opportunities to explain decisions, and make time for questions and feedback. For certification-related topics, this listing describes training or study support, not an official credential or a claim of authorized training-partner status. Check the current awarding-body requirements and exam version with your advisor. Contact MindClick for the delivery format, teaching language, instructor profile, duration, dates, and a written fee proposal. These details are confirmed for your individual or team inquiry rather than assumed from another provider’s program.

What you’ll learn

Explain tokens attention and transformer architecture in a practical work context.
Make informed choices about using pre-trained language models.
Recognize common pitfalls in retrieval grounding and evaluation.
Connect inference privacy and deployment constraints to a practical action plan.

A CLEAR PATH FORWARD

Recommended learning plan

4-day learning plan

Learn through a practical data or AI use case, with focused discussions, practical exercises and work you can take away.

Learning route: 4 recommended days
Practical work: 4 guided activities
Finish with: A practical review

Take a look at each day. Open a section to see the topics and practical work.

Day 1Tokens attention and transformer architecture

What we’ll cover

Tokens attention
Transformer architecture

Put it into practice

Use a practical data or AI use case to work through the options, explain your reasoning and compare approaches with the group.

You’ll take away
A practical worksheet and next-step checklist

Day 2Using pre-trained language models

What we’ll cover

Using pre-trained language models

Put it into practice

Work through a sample dataset or model, challenge its assumptions and explain what the results do and do not show.

You’ll take away
An analysis with assumptions and quality checks

Day 3Retrieval grounding and evaluation

What we’ll cover

Retrieval grounding
Evaluation

Put it into practice

Compare two financial scenarios and trace the calculation back to its assumptions and source inputs.

You’ll take away
A checked calculation and decision summary

Day 4Inference privacy and deployment constraints

What we’ll cover

Inference privacy
Deployment constraints

Put it into practice

Review a realistic dilemma and identify what can be used, what needs permission and where human review is essential.

You’ll take away
A decision log with clear safeguards

Bring it all together

Present the result alongside your evaluation, limitations and next steps.

Prerequisites

Bring a laptop. Coding-focused courses use Python; business-focused courses work with examples and decision tools.

The course outline covers tokens attention and transformer architecture, using pre-trained language models, retrieval grounding and evaluation, inference privacy and deployment constraints. Discuss the depth and emphasis you need with a MindClick advisor.

Can I request this training from the UAE or Saudi Arabia?

Yes. MindClick accepts training inquiries from individuals and teams in the UAE and Saudi Arabia. Confirm online or in-person delivery, the venue if applicable, teaching language, and dates before booking. UAE uses UTC+4; Saudi Arabia uses UTC+3.

Does this listing include an official qualification or exam voucher?

This is a training or study-support listing, not an official qualification award. Certification names identify the subject of preparation, not a partner endorsement. Confirm current eligibility, authorized delivery arrangements where required, and exam-voucher inclusion separately.

What are the fees and duration?

Contact MindClick for a written proposal based on your learning needs and delivery format. Confirm the training fee, applicable taxes, duration, materials, exam fees if relevant, and any additional charges before enrolling.

How do I choose the right starting point?

Share your current role, experience, goals, and preferred learning format. Ask about prerequisites and the current curriculum before booking. Training does not guarantee an exam pass, certification, or employment.

Neural networks and deep learning

60 hoursAdvanced

$1,799 USDView course