Three Programmes
What We Teach and How Each Programme Works
Detailed descriptions of each programme — what you will cover, how long it takes, what the practical work involves, and what each one costs.
Back to HomeOur Methodology
How Our Programmes Are Structured
Each programme at Pikanet Code is built around the same underlying principle: written material is the primary medium, applied exercises are integrated throughout, and the difficulty level is set to produce genuine understanding rather than surface familiarity.
We do not build our programmes around video lectures. The material is written for careful reading, with the expectation that students will re-read sections, work through the exercises, and take whatever time is needed before moving forward. There is no schedule to keep.
Practical work uses current, publicly available tools. We do not build proprietary environments that become outdated — we use what practitioners use, and we update the exercises when the tools evolve.
Written Material First
Structured documents designed for careful reading, not passive video consumption.
Applied Exercises Integrated
Practical work is part of the structure, not an afterthought or optional extra.
Self-Paced, No Deadlines
Progress at a rate that suits you. No cohort, no expiry, no pressure.
Maintained for Currency
Content is updated as the field moves. You read current material, not a time capsule.
Process Overview
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01
Text Representation — Vector spaces, embeddings, tokenisation, and how text becomes something a model can work with.
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02
Transformer Architecture — Attention mechanisms, encoder-decoder design, positional encoding explained in depth.
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03
Language Tasks — Classification, generation, summarisation, and retrieval using current open models.
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04
Applied Exercises — Hands-on work with real datasets and open-source model libraries throughout.
Natural Language Processing Course
A focused course on natural language processing as practised in the era of transformer-based models, covering the foundational concepts of text representation, the architecture of attention-based networks, and the contemporary approaches to common language tasks.
The course combines structured written material with applied exercises that allow students to work directly with current open models and datasets. Suitable for learners with foundational machine learning knowledge who would like a careful path into the language side of the field.
What the course covers
- Token-level and sentence-level text representation
- Self-attention and multi-head attention explained from first principles
- Pre-training, fine-tuning, and instruction tuning in context
- Practical classification, generation and retrieval tasks
- Evaluation methods for language model outputs
AI Ethics & Responsible Development Programme
A programme covering the careful thinking required around the development and deployment of AI systems, including questions of fairness, transparency, environmental impact, and the broader social considerations that arise when these systems are placed into the world.
The programme combines structured readings with discussion sessions and short written assignments. Suitable for learners who recognise that the technical work of AI development is interwoven with questions that warrant patient, careful thought rather than quick answers.
What the programme covers
- Fairness frameworks and algorithmic bias in practice
- Transparency, explainability, and what accountability actually requires
- Environmental costs of large-scale model training and deployment
- Regulatory context in Southeast Asia and internationally
- Short written assignments to develop analytical thinking
Programme Structure
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01
Foundations — What ethical analysis involves and why technical expertise alone is insufficient.
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02
Fairness and Bias — Definitions, measurement approaches, and the tensions between competing fairness criteria.
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03
Deployment Considerations — Impact assessment, accountability structures, and real-world case analysis.
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04
Written Assignments — Short structured exercises to develop the habit of careful, evidence-based reasoning.
Track Structure
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01
Pipeline Design — Data flow, component contracts, versioning, and the architecture of a maintainable ML system.
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02
Deployment Practices — Serving infrastructure, rollout strategies, rollback procedures, and latency considerations.
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03
Monitoring and Drift — Detecting model degradation, data distribution shift, and building feedback loops for ongoing maintenance.
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04
Practical Project — Substantial applied work building components of a realistic production pipeline from design through deployment.
Production AI Systems Track
A practical track focused on the development of AI systems intended for ongoing production use, with attention to the engineering practices that allow such systems to be developed, deployed, monitored, and maintained over time.
The track combines structured material on systems engineering with substantial practical work building components of a realistic production pipeline. Suitable for learners who have completed foundational AI study and now wish to develop the systems-level understanding that production deployment requires.
What the track covers
- ML system architecture for production environments
- Model serving, deployment infrastructure, and rollout strategy
- Monitoring for model performance and data drift
- Feedback loops and maintenance lifecycle management
- Substantial practical project building a production-ready pipeline
Choose Your Path
Which Programme Fits You
Use this comparison to identify the right starting point. The programmes are independent — each stands on its own — though they are designed to complement each other if you study more than one.
| Feature | NLP Course | AI Ethics | Production Track |
|---|---|---|---|
| Price (THB) | ฿3,100 | ฿5,800 | ฿7,600 |
| Technical depth | High | Low–Medium | High |
| Coding required | |||
| Prior AI knowledge needed | Foundational ML | Helpful, not required | Beyond foundational |
| Practical project | Exercises | Written assignments | Substantial project |
| Typical duration | 6–8 weeks | 6–8 weeks | 10–14 weeks |
| Best for | ML engineers entering NLP | Technical & non-technical practitioners | Engineers building for production |
Across All Programmes
Standards That Apply to Everything We Deliver
Data Privacy
Student data is held securely and used only for the purpose of delivering and supporting the enrolled programme. Never shared commercially.
Content Maintenance
All programmes undergo regular review. When content becomes inaccurate due to field changes, it is updated — not left to drift.
Responsive Support
Questions reach someone who knows the material. Target response within one working day. No automated triage or forum redirect.
Accuracy First
Where the field has genuine uncertainty, we say so. We do not smooth over complexity to make material appear more authoritative than it is.
Transparent Pricing
All prices are displayed in Thai Baht and paid once at enrolment. No subscriptions, no add-ons, no unexpected charges.
Open Tools Only
Practical exercises use publicly available open-source tools and datasets. No proprietary environments, no licence fees, no platform lock-in.
Enrolment Pricing
Choose Your Programme
All prices in Thai Baht (฿). One-time payment, full access, no expiry.
NLP Course
Natural Language Processing
฿3,100
one-time
- Full written course material
- Integrated applied exercises
- Direct author support
- Access to maintained updates
AI Ethics Programme
Responsible Development
฿5,800
one-time
- Full written programme material
- Structured written assignments
- Direct author support
- Access to maintained updates
Production Track
AI Systems Engineering
฿7,600
one-time
- Full written track material
- Substantial production project
- Direct author support
- Access to maintained updates
Not sure where to start?
Describe your background and we will help you choose
If you are unsure which programme fits where you are now, send us a message. We will give you a straightforward recommendation without any pressure.
Get in Touch