A university AI course had become overwhelming; after recurring 1:1 support, the material became understandable and the student recovered academically.
Parent of a university AI studentVerify on SuperProf
Tutoring
Machine Learning Tutor
Pristone Academy provides live, online, one-on-one machine learning tutoring from an MIT-trained senior engineer who builds production ML systems. Bring a course concept, a project, or a model that is not behaving as expected. We trace the first weak link—from problem definition and data splitting through pipelines, evaluation, error analysis, and deployment—and build the missing understanding around your own work.
Premium private coaching, typically structured as multi-session engagements.
This is premium, live 1:1 instruction—not an automated homework service. The paid diagnostic is billed hourly, with the exact rate confirmed before scheduling. It clarifies your starting point, project scope, and appropriate learning cadence before an ongoing plan is recommended.
Replies within 2 hours.
The Pristone Method
One capability loop across every track.
- Diagnose
- Prioritize
- Build
- Test transfer
- Measure
- Re-prioritize
Who this is for
Primarily university students and working engineers who need to understand, build, or debug a machine learning project. Researchers and serious self-learners are also a fit when they need practical implementation help alongside the underlying math.
- Students who need to understand an ML course or implement a project responsibly
- Engineers transitioning into ML, data science, or MLOps roles
- Builders debugging data pipelines, evaluation, or model behavior
- Self-learners who want the math and the workflow—not just API calls
What you walk away with
- ✓A real grasp of the math behind models, not just library calls
- ✓A leakage-safe workflow for preprocessing, training, and tuning
- ✓The ability to choose metrics that match the real decision
- ✓A reproducible project you can explain, debug, and improve
Who this is not for
Being upfront about fit saves everyone time — including yours. This program is not a good match if:
- Anyone seeking completed graded work, exam answers, or code to submit as their own
- People looking only for an automated answer generator
- Learners unwilling to explain, test, and revise their own work
- Teams seeking outsourced production development rather than instruction
Relevant verified feedback
Read the teaching record before you book.
These are privacy-safe paraphrases of feedback published on independent tutoring marketplaces. No student names or identifying details are reproduced here.
The instruction was consistently adapted to the learner's current level and preferred way of learning.
Artificial intelligence learnerVerify on SuperProf
How live machine learning tutoring works
1
1. Goal, code, and data review
Start with the course objective, project decision, notebook, error, or model result you need to understand. We identify what is known, what is assumed, and where the workflow first becomes unreliable.
2
2. Concept reconstruction
Rebuild the necessary intuition in linear algebra, probability, loss functions, regression, classification, trees, clustering, or neural networks—only as deeply as your goal requires.
3
3. Leakage-safe implementation
Turn the idea into a reproducible pipeline. Define the target and baseline, split data appropriately, fit learned transformations on training data, and keep validation evidence honest.
4
4. Evaluation, debugging, and next experiment
Choose decision-aligned metrics, inspect errors and meaningful slices, explain why the model behaves as it does, and leave with one bounded experiment or study task to complete next.
Diagnostic map
What an ML project diagnostic reveals
Problem and data contract
Whether the target, prediction unit, observation time, label availability, and deployment constraints describe a problem the model can actually solve.
Split and leakage safety
Whether preprocessing, feature selection, tuning, and validation keep future or held-out information from influencing training.
Evaluation judgment
Whether the baseline, metric, threshold, and validation design reflect the costs of false positives, false negatives, and distribution shift.
Reproducibility and delivery
Whether someone else can recreate the result and whether the model has a clear input contract, monitoring plan, and fallback behavior.
Find the first weak link in your ML workflow.
A diagnostic reviews the problem definition, data split, pipeline, evaluation, and explanation so your next step is specific.
Frequently asked questions
No. We meet you where you are and build the math you need alongside the models, so concepts click instead of feeling like a prerequisite wall.
Yes. Sessions are live and private over video. You can share your reasoning, code, diagrams, or results and receive feedback while you work through them.
Bring the goal, relevant instructions or requirements, and the work you have already attempted. For a project, that may include a data dictionary, notebook, repository, error message, or evaluation result—with private or sensitive data removed.
Both, balanced to your goals. You get the underlying intuition and hands-on implementation, taught by an engineer who ships real ML rather than only teaching it.
Yes. Bring your syllabus, problem set, research question, or work project, and the session will focus on what you need to understand and implement. Tutoring supports your learning; it does not complete graded work or confidential employer work for you.
Edward Mabonga — MIT EECS, senior software engineer with production AI/ML experience, and founder of Pristone Academy. All sessions are premium and one-on-one.
Before you book
Clear answers and clear scheduling policies.
Ask first, free
Send Edward a question before paying or scheduling. Replies within 2 hours.
Rescheduling
Reschedule with at least 24 hours' notice at no charge.
Cancellation and no-shows
Cancel with at least 24 hours' notice. Cancellations inside 24 hours and no-shows count as used unless an emergency exception is agreed.
Satisfaction and outcomes
No score, admission, job, or other outcome is guaranteed. If a session materially differs from its published scope, contact Pristone within 48 hours so it can be reviewed and an appropriate correction proposed.
Explore next
Learn machine learning from someone who ships it.
Book a diagnostic, tell us your goals and level, and we'll build a private ML tutoring plan around them.
Founder-led by Edward Mabonga (MIT EECS) · 250+ five-star reviews · 1,000+ tutoring hoursView verified reviews