Three Courses, One Direction
Python to machine learning to deep learning — a structured pathway for learners who want to understand each step before moving to the next.
Back to HomeHow the Courses Are Structured
Each course at Synapsy follows the same basic structure: recorded lessons explain the concept, a notebook exercise gives you something to run and modify, and at certain points in the course you submit a notebook for written review by a course educator.
The three courses are sequenced deliberately. Python Foundations lays down the vocabulary and habits you need to read ML code fluently. The Machine Learning Pathway works through classical algorithms with enough depth that you understand when and why each one fits a particular problem. The Deep Learning Bootcamp builds on both to cover neural network fundamentals and deployment of a working model.
We are direct about prerequisites. The ML Pathway requires a Python foundation, and the Bootcamp requires prior programming experience. If you are not sure whether your background is sufficient, send us an enquiry and we will tell you honestly.
Python Foundations
฿8,500
A self-paced eight-week online course covering the fundamentals of Python for those who want a careful, considered start in programming. The course covers data types, control flow, functions, working with files, common data structures, and a short introduction to libraries used in data work.
Each week includes recorded lessons, short exercises, and an end-of-week practice notebook. Students receive written feedback on three submitted notebooks. Suitable for learners who can dedicate roughly four to six hours per week and prefer a calm pace.
What the course covers
- Data types and variables — integers, strings, lists, dictionaries
- Control flow — conditionals, loops, and when to use each
- Functions — writing them, reading them, and knowing what they return
- Working with files and reading tabular data
- Introduction to NumPy and Pandas
How the eight weeks run
Weeks 1–2: Data types, variables, and the Python interactive environment. Getting comfortable reading and running code.
Weeks 3–4: Control flow and functions. First notebook submission for written feedback.
Weeks 5–6: Common data structures and file handling. Second notebook submission.
Weeks 7–8: Introduction to NumPy and Pandas. Third and final notebook submission with feedback.
Machine Learning Foundations Pathway
฿19,500
A twelve-week pathway in classical machine learning concepts and practice, intended for students who have a Python foundation. The pathway covers regression and classification, feature engineering basics, cross-validation, common algorithms and when each is suited, and how to read evaluation metrics carefully.
Weekly recorded lessons are paired with practical notebooks and three small portfolio projects. Mentors hold a weekly office hour over video for questions. Suited for learners with roughly six to eight hours per week to spend.
Topics covered across the pathway
- Regression — linear, polynomial, and regularisation basics
- Classification — logistic regression, decision trees, and random forests
- Feature engineering — what to encode, what to scale, what to drop
- Cross-validation and train/test split practice
- Reading evaluation metrics — accuracy, precision, recall, F1, AUC
- Three small portfolio projects on real datasets
How the twelve weeks run
Weeks 1–3: Regression and the idea of a model. First portfolio project assigned.
Weeks 4–7: Classification algorithms and feature engineering. Second portfolio project assigned.
Weeks 8–10: Cross-validation and evaluation. Deepening work with scikit-learn.
Weeks 11–12: Third portfolio project — a full classification pipeline on a new dataset.
prerequisite: Python Foundations or equivalent
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Deep Learning Project Bootcamp
฿33,800
A sixteen-week intensive bootcamp focused on practical deep learning. The bootcamp covers neural network fundamentals, training stability, common architectures for vision and language, deployment of a small model, and the production of a portfolio-ready capstone project.
Weekly recorded lessons are paired with hands-on notebooks, two mentor reviews of student code, and a final capstone presentation reviewed by a panel of three educators. Suitable for committed learners with prior programming experience.
Topics covered in the bootcamp
- Neural network fundamentals — layers, activations, backpropagation
- Training stability — learning rate, batch size, overfitting
- Convolutional networks for image classification
- Transformer-based architectures for text
- Deploying a small model as a working endpoint
- Capstone project with panel review by three educators
How the sixteen weeks run
Weeks 1–4: Neural network foundations. First mentor code review of student notebook.
Weeks 5–9: Vision architectures and training techniques. Hands-on image classification project.
Weeks 10–13: Text architectures and model deployment. Second mentor code review.
Weeks 14–16: Capstone project — build, document, and present to a three-person panel.
prerequisite: prior programming experience required
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Deciding Where to Start
A side-by-side look at all three courses to help you choose.
| Python Foundations | ML Pathway | DL Bootcamp | |
|---|---|---|---|
| Duration | 8 weeks | 12 weeks | 16 weeks |
| Hours per week | 4–6 hrs | 6–8 hrs | 8–12 hrs |
| Prerequisite | None | Python foundation | Programming experience |
| Notebook feedback | 3 submissions | Included | 2 mentor reviews |
| Weekly office hours | — | Yes | Yes |
| Portfolio projects | — | 3 projects | Capstone + panel |
| Price | ฿8,500 | ฿19,500 | ฿33,800 |
| Best for | Complete beginners to programming | Those comfortable with Python, new to ML | Experienced programmers, ready for deep learning |
Not sure which row you are on? Send an enquiry and describe your current coding background. We will tell you honestly where to start.
What Applies to Every Course
Tested, Compatible Code
Every notebook is tested against the Python and library versions listed in the course setup guide before the cohort opens. The code you are asked to run, runs.
Secure Student Records
Enrolment data is stored with access restricted to Synapsy staff who need it for course administration. We do not share student contact information with third parties.
Annual Content Review
All course material is reviewed each year. Code examples, library references, and explanations that have become outdated are revised before the next cohort opens.
Educator-Written Feedback
Notebook reviews are written by the course educator, not generated automatically. Comments reference the specific notebook you submitted.
Managed Cohort Sizes
Enrolment in each cohort is capped to keep the review and office-hours workload at a level where individual attention is still possible.
Transparent Course Pages
Each course page states what is covered, how long it takes, what the prerequisites are, and what the price includes. No information is withheld behind a registration wall.
Course Fees
All prices in Thai Baht. Fees are paid in full before enrolment.
Python Foundations
฿8,500
- Recorded weekly lessons
- 8 exercise notebooks
- Written feedback on 3 submissions
- No prerequisites required
ML Foundations
฿19,500
- Recorded weekly lessons
- Practical notebooks throughout
- Weekly video office hours
- 3 portfolio projects
Deep Learning Bootcamp
฿33,800
- Recorded weekly lessons
- Hands-on notebooks
- 2 mentor code reviews
- Capstone with panel review
Tell us where you are starting from
We are happy to say plainly which course makes sense for your current level. Use the contact form and describe your programming background in a sentence or two.
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