// course catalogue
Three Courses,
One Coherent Path
From Python fundamentals to applied large language model development — each course is designed to stand alone or connect to the next one.
Back to Home// our approach
How the Courses Work
Every Pim Tech course follows the same basic rhythm — structured weeks, recorded sessions, real assignments, and honest estimates of what you will spend time on.
Weekly Structure
Each week has a session, reading, and a small assignment. You always know what the week involves before it starts.
Recorded Sessions
Review sessions are recorded and available to enrolled students — watch when your schedule allows, not only at a fixed time.
Graded Assignments
Assignments are reviewed by instructors, not automated. Feedback is specific to what you submitted, not generic comments.
Applied Projects
NLP and LLM tracks include graded projects and portfolio work that reflect real tasks — not simplified pedagogical exercises.
// course 01
Python for Data Work Bootcamp
An introductory bootcamp for learners new to programming or new to programming specifically for data work. The course covers the Python language, the standard data libraries — NumPy, pandas, and the surrounding ecosystem — and the practical patterns of working with structured data from a notebook. It is paced for learners balancing study with other commitments, with small weekly assignments and recorded review sessions.
- Python language fundamentals — syntax, data structures, functions, modules
- NumPy and pandas for structured data manipulation
- Notebook-based workflows for exploration and reporting
- Weekly small assignments, recorded review sessions
How the Course Unfolds
// course 02
Natural Language Processing Course
A focused course covering the practical foundations of working with text data. The curriculum moves through tokenization and text preprocessing, word and sentence embeddings, classical sequence models, and then into transformer-based approaches. The emphasis throughout is on building working models for common text tasks — not on survey coverage of every possible method. Learners complete two graded projects and have access to the course community throughout.
- Tokenization, preprocessing, and text representation
- Word2Vec, sentence embeddings, and similarity tasks
- Introduction to transformer-based approaches and fine-tuning
- Two graded projects, online community access
How the Course Unfolds
// course 03
Large Language Model Application Track
An applied track for learners who want to build applications that use large language models. The course covers prompt design, retrieval-augmented generation, evaluation approaches, and the engineering considerations that distinguish robust applications from early prototypes. It is designed for learners who have completed prior programming and machine learning coursework and who want to move toward applied, portfolio-backed work. The track includes a substantial portfolio project with structured mentor review.
- Prompt design — structure, system prompts, few-shot patterns
- Retrieval-augmented generation — chunking, indexing, retrieval pipelines
- Evaluation — measuring output quality, building test sets
- Portfolio project with mentor review at defined checkpoints
How the Track Unfolds
// decide which course fits
Course Comparison
Use this matrix to understand which course is appropriate for your current background and goals.
| Feature | Python Bootcamp | NLP Course | LLM Track |
|---|---|---|---|
| Prior coding required | No | Yes — Python | Yes — Python & ML |
| Duration | 10 weeks | Varies | Varies |
| Weekly assignments | |||
| Graded projects | 1 final | 2 | Portfolio project |
| Online community access | |||
| Mentor review | |||
| Price (Thai Baht) | ฿3,800 | ฿14,500 | ฿29,500 |
| Best for | New to Python or data work | Building text models with existing ML knowledge | Applied LLM engineering with portfolio work |
// our standards
Across All Courses
Student Data Privacy
Enrolment details and assignment work are not shared with third parties for commercial purposes. Privacy policy available at privacy-policy.html.
Consistent Grading Criteria
Assignment rubrics are shared with students before the assignment is due. Grading criteria do not change between when rubrics are distributed and when work is reviewed.
Communication Standards
Email enquiries from enrolled students are answered within two working days. Community questions from NLP and LLM students receive instructor responses within one working day.
Annual Content Review
Course materials are reviewed each year before a new cohort starts. The LLM track receives the most significant updates, reflecting the rate of change in the field.
Transparent Fee Structure
Fees are listed in Thai Baht and include everything described in the course outline. There are no additional charges for community access, recorded sessions, or assignment review.
Cohort Size Limits
We maintain limits on cohort size to preserve the quality of instructor attention. When capacity is reached, new enrolments are directed to the next available cohort.
// course fees
Pricing
All fees stated in Thai Baht. No additional costs for materials, community access, or recordings.
// entry level
Python Bootcamp
- 10-week structured programme
- Weekly assignments with instructor review
- Recorded sessions included
- Final assignment
NLP Course
- Weekly assignments throughout
- Two graded projects
- Online community access
- Recorded sessions
LLM Application Track
- Applied track with portfolio project
- Structured mentor review at checkpoints
- Community access included
- Recorded sessions
// questions?
Not Sure Where to Start?
If you are unsure whether the Python bootcamp covers the right ground for your background, or whether you are ready for the NLP course, send us a message. We will give you a direct answer based on what you describe.
Ask a Question