— Our Pathways
Three courses, described
honestly and in detail
What each course covers, who it is suited for, how long it takes, and what it costs. No broad promises — just the specifics.
Back to Home— How We Teach
How every Gradientco course is built
Each course follows the same production approach. Lessons are recorded so they can be watched at any time. Every lesson has a paired Jupyter notebook — worked through step by step — rather than a static summary. Practice problems come with written solutions, not just answer keys.
For the Computer Vision and NLP courses, mentor office hours are held via video call on a fixed schedule. These are small group sessions where learners can ask questions from the material or from their own work. A recording is made available after each session.
Project briefs are written to be specific enough to produce a coherent piece of work, and loose enough that the design decisions are yours. Written feedback from an educator addresses the reasoning behind your choices, not just the technical output.
Pathway 01
Mathematics for Machine Learning
A self-paced ten-week pathway covering the mathematical foundations most often used in machine learning practice: linear algebra basics, calculus essentials, probability and statistics, and an introduction to optimization. Each week includes recorded lessons, hand-worked example notebooks, and a small practice set.
Suited for learners who would like a calm, careful refresher in the mathematics before or alongside a practical course.
What is covered
- Linear algebra: vectors, matrices, eigenvalues, and singular value decomposition
- Calculus: derivatives, chain rule, partial derivatives, and the gradient
- Probability and statistics: distributions, expectations, and Bayes' theorem
- Introduction to optimization: gradient descent and its variants
- Hand-worked notebooks and practice sets for each week
What it includes
Pathway 02
Computer Vision Practical Course
A fourteen-week practical course on computer vision for learners with a Python and machine learning foundation. Topics include image processing fundamentals, convolutional architectures, transfer learning, careful evaluation, and the assembly of two small applied projects.
Weekly recorded lessons are paired with hands-on notebooks and mentor office hours over video. Two of the projects are reviewed in writing by an educator, and the final piece becomes part of the student's portfolio. Suited for committed learners with roughly seven hours per week to spend.
What is covered
- Image processing: filtering, transforms, and feature extraction
- Convolutional neural networks: architecture, training, and debugging
- Transfer learning and fine-tuning on custom datasets
- Evaluation methodology and error analysis
- Two applied projects, one of which forms a portfolio piece
What it includes
Pathway 03
Natural Language Processing Pathway
A sixteen-week pathway in natural language processing for learners with a Python and machine learning foundation. Topics include classical text processing, embeddings, modern transformer architectures, careful evaluation of language tasks, and the production of a small applied portfolio project.
Weekly recorded lessons are paired with notebooks, mentor office hours over video, and one written code review during the course. Suited for learners with roughly seven to nine hours per week to spend.
What is covered
- Classical NLP: tokenisation, n-grams, TF-IDF, and named entity recognition
- Word embeddings and contextual representations
- Transformer architecture in depth: attention, encoders, and decoders
- Fine-tuning pre-trained language models on classification and generation tasks
- Evaluation frameworks for language tasks
- Applied portfolio project with structured brief
What it includes
— Which Course
Choosing the right pathway
Use this table to see what each course includes. The mathematics pathway can be studied on its own or alongside one of the practical courses.
| Feature | Maths ML | Computer Vision | NLP |
|---|---|---|---|
| Duration | 10 weeks | 14 weeks | 16 weeks |
| Recorded lessons | |||
| Worked notebooks | |||
| Mentor office hours | |||
| Written project review | 2 reviews | 1 review | |
| Portfolio project | |||
| Prior Python required | |||
| Price (THB) | ฿9,200 | ฿20,500 | ฿31,500 |
Best for
Maths ML Pathway
Learners who want to consolidate mathematical understanding before or alongside applied work. No programming required.
Best for
Computer Vision Course
Learners with Python and basic ML knowledge who want to work on applied vision problems and build a project for their portfolio.
Best for
NLP Pathway
Learners with Python and ML experience who want a structured course in language processing up to and including transformers.
— Course Standards
What we keep consistent across all pathways
Accurate course descriptions
Every course page states the assumed prior knowledge, the realistic weekly hours, and exactly what the fee includes.
Annual content review
All courses are reviewed each year. Notebooks are tested against current library versions and topics are updated where the field has moved on.
Data privacy
Learner information is used for course delivery and requested communication only. It is not shared with third parties.
Educators with applied experience
Course authors work in applied ML alongside their teaching role. Material reflects current practice, not only curriculum tradition.
Support response time
Enquiries receive a reply within one business day. Access issues are prioritised and addressed on the same day where possible.
Materials yours to keep
All notebooks, practice sets, and written feedback remain accessible after the course ends. There is no expiry on course access.
— Pricing
Fees, stated clearly
Pathway 01
Mathematics for ML
฿9,200
one-time enrolment
- 10 weeks of lessons
- Worked notebooks
- Weekly practice sets
- Completion record
Pathway 02 — Most requested
Computer Vision
฿20,500
one-time enrolment
- 14 weeks of lessons
- Hands-on notebooks
- Mentor office hours
- 2 written project reviews
- Portfolio project
Pathway 03
NLP Pathway
฿31,500
one-time enrolment
- 16 weeks of lessons
- Worked notebooks
- Mentor office hours
- 1 written code review
- Portfolio project
— Enquiries
Not sure which pathway to choose?
We're happy to discuss where you are now and which course would be the most useful starting point. Reach us by message or by phone.