What students say about studying here.
Reviews from developers who have completed our courses or participated in the reading group.
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The Foundations course is written in a way that actually explains what is happening. I had tried a couple of video courses before and always felt like I was following along without understanding. The notes here are more like reading a technical book — a bit slower, but it sticks. The worked notebooks were the most useful part for me.
I did the Foundations course as preparation before moving into an ML-adjacent role. It covered gradient descent in more depth than I expected, which was exactly what I needed. The exercises require you to actually implement things, not just answer multiple-choice questions — that makes a difference. My one note: some weeks felt denser than others, so pacing your schedule helps.
The LM Engineering course is genuinely technical. I came in knowing the theory but without hands-on experience with Hugging Face at this level. The RAG module in particular was comprehensive — the code reference showed a complete working pipeline, not a toy example. I used parts of it directly in a work project two weeks after finishing the course.
I have been attending the reading group for six months. It is exactly what it says it is — a structured, monthly session where someone who has actually read the paper carefully walks you through it. The pre-session notes make it possible to participate meaningfully even when the paper is difficult. I would not have had the patience to read many of these papers alone.
I took the Foundations course as my first serious study of machine learning. The sequencing is logical — you are not thrown into neural networks on week one. The reference solutions are helpful because they show the reasoning, not just the code. I think some weeks I needed more time than allocated, but that is on me for underestimating the density of the material.
I did the LM Engineering course and the reading group together. The course gave me the practical foundation I needed; the group helped me stay current with what is being published. The instructors respond in the forum with actual explanations, not just links. That responsiveness made a difference when I was working through the fine-tuning module.
Student Journeys
Starting point
A backend engineer with five years of Python experience wanted to move into an ML engineering role. Had read introductory material online but felt the gap between "knowing what gradient descent is" and "knowing when and why to use it" was still wide.
Course experience
Completed the Foundations course over 14 weeks (two weeks longer than scheduled). Found the loss function and optimisation modules most useful. Used the discussion forum regularly during the model evaluation section.
Outcome
Moved into an ML role within three months of completing the course. Reports that the course gave a working vocabulary and enough practical depth to contribute to code reviews and architecture discussions in the new team.
"I finally understood what I was actually doing in sklearn, not just what functions to call."
Starting point
An ML engineer with theoretical knowledge of transformers but no hands-on experience building retrieval systems. Was tasked with scoping a document-search feature for a product but found available tutorials too shallow for production use.
Course experience
Completed the LM Engineering course in the standard 8 weeks. Found the RAG and structured output modules directly applicable. The fine-tuning module required more compute than expected — ran experiments on Kaggle notebooks to manage cost.
Outcome
Delivered the document-search feature prototype within six weeks of finishing the course. The pipeline used patterns from the RAG module with modifications for their specific data structure. Reduced search result latency by roughly 40% compared to the initial naive implementation.
"The code reference for the RAG module saved me weeks of reading scattered documentation."
Starting point
A researcher who had completed the Foundations course wanted to stay connected to new work being published. Found that reading papers alone was slow and that she often struggled to assess which claims were well-supported and which were speculative.
Group experience
Joined the reading group mid-year. Found the pre-session notes a practical way to prepare. Appreciated that the moderator explicitly flagged sections of papers where the experimental setup was weak or the conclusions outran the evidence.
Outcome
After six sessions, reports a noticeably faster reading pace and better ability to identify the core contribution of a paper early. Has begun reading papers outside the group's selection using the same critical framework discussed in sessions.
"The reading group taught me how to read a paper, not just what a particular paper says."
Contact
Watthana, Bangkok 10110
Sat: 10:00–14:00 ICT
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