What are the best AI and programming classes in Taree?
Online lessons in which a Taree teenager or adult learns how machine learning really behaves, coding models in Python with a teacher and testing them on open New South Wales data. The 2021 Census counted 26,519 people in the Taree urban area, with a median age of 46. Every class runs live over video, either private or with a few learners at a similar stage, and the opening session is free.
Among the 42 public schools in the Mid-Coast council area, only 8 teach secondary years. Train a simple model to pick those 8 from their location and founding year and it scores 76% accuracy, which sounds fine until you notice that answering "primary" every time scores 81%, and that the model finds only 3 of the 8. A popular fix, SMOTE, invents extra secondary schools between real ones so the model sees more of them. Recall doubles to about 76%. The price is a crop of false alarms. Our Taree project builds both models in Python and weighs that trade honestly.
Facts last verified 9 October 2026. Teaching is online; no Taree branch is claimed.
Each course builds towards the imbalance project on this page. The free first lesson recommends one, and we do not ask for a card.

Machine learning from the ground up: data, simple models, fair testing and honest results.
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Python for real datasets, from loading and cleaning to measuring what a model gets wrong.
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A complete machine learning course, including imbalanced data, resampling and evaluation metrics.
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The full path from raw data to models whose numbers survive scrutiny.
See the syllabusBrowse the course atlas for more than one hundred options and use the coding roadmap to check prerequisites.
The four we are known for
These run underneath everything above. Every one is live and online, placed by ability rather than by age, and the first class is free.

Python, web and AI projects where the learner still owns the thinking.
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Automate the work you already do, then let AI carry part of it.
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Train a model, read what it learned, and be able to say why it is wrong.
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Run AI coding agents on real work without losing control of the codebase.
See the syllabusCounts for the Taree Significant Urban Area as QuickStats printed them after the 2021 count, set beside the state and national figures.
| Total | Taree |
|---|---|
| Residents | 26,519 |
| Families | 6,858 |
| Private dwellings | 11,774 |
| People per household on average | 2.3 |
| Measure | Taree | NSW | Australia |
|---|---|---|---|
| Median age | 46 | 39 | 38 |
| Occupied homes that are separate houses | 83.4% (8,690) | 65.6% | 72.3% |
| Occupied homes in a flat or apartment block | 7.0% (725) | 21.7% | 14.2% |
| Workers employed as technicians or in a trade | 13.4% (1,235) | 11.9% | 12.9% |
Taree's median age of 46 is seven years above the New South Wales median of 39.
8,690 occupied homes, 83.4%, were separate houses, well above the state share of 65.6%.
The Census recorded 1,703 Taree residents aged 10 to 14 and 1,482 aged 15 to 19.
How to read the tables: small random changes are applied to census counts to stop individuals being identified, so a table's parts can miss its total by a little. Totals and percentages appear here exactly as the bureau published them, and we never add parts together to make a total.
The NSW Department of Education lists every government school with its level, founding date and coordinates. For the 42 schools in the Mid-Coast council area we asked a nearest-neighbour model which ones teach secondary years, judged it fairly by leaving each school out in turn, and then tried the SMOTE fix.
| Method | Accuracy | Secondary schools found | False alarms |
|---|---|---|---|
| Always answer "primary" | 81.0% | 0 of 8 | 0 |
| 3-nearest-neighbour model | 76.2% | 3 of 8 | 5 |
| Same model trained with SMOTE, average of 20 runs | 74.8% | about 6 of 8 (75.6%) | about 9 |
| Measure | Lowest | Average | Highest |
|---|---|---|---|
| Recall, share of the 8 found | 62.5% | 75.6% | 87.5% |
| Precision, share of alarms that were right | 35.3% | 41.3% | 50.0% |
Turn each school into three numbers, its first-teacher year and its latitude and longitude, scaled so no one number dominates. The label is whether it teaches Years 7 and above.
Leave one school out, train on the other 41, predict the missing one, and repeat 42 times. Accuracy alone hides the problem: the model misses most secondary schools.
SMOTE picks a secondary school, picks one of its nearest secondary neighbours, and creates a new point somewhere on the line between them. The model now sees a balanced picture.
Every synthetic point SMOTE creates is a school that does not exist, with a made-up founding year and a location somewhere between two real towns. That is why it can help a model notice the rare class, and also why it raises false alarms: the model has learned from places that are not there. In the Taree runs recall roughly doubled while precision sat near 41%, so most alarms were wrong. Whether that trade is worth it depends entirely on what a missed case costs compared with a false one.
We ran every score above ourselves, in Python, on that open list. Treat the task as practice in imbalanced classification: the model sees founding years and coordinates only, so it can say nothing about how good, big or busy any school is. Synthetic points exist only inside the training code and never describe a real place.
Rare cases are where AI matters most and where it is easiest to fool yourself. The same habits apply from a 42-school exercise to a system screening millions of records.
| Question | In the Taree project | Wider lesson |
|---|---|---|
| Does high accuracy mean a good model? | Doing nothing scored 81% | Always compare with a trivial baseline |
| Which errors matter? | 5 misses before SMOTE, about 9 false alarms after | Report recall and precision, not just accuracy |
| Does the result depend on chance? | Recall ranged from 62.5% to 87.5% across seeds | Repeat runs and report the spread |
| Was the test fair? | Synthetic points made only from training data | Never let invented or test data leak into training |
An AI assistant asked to "fix class imbalance" will often apply SMOTE to the whole dataset before splitting it, which lets synthetic copies of test schools leak into training and makes the results look better than they are. Learners who have built the Taree evaluation fold by fold know to look for that mistake, and to ask what a false alarm actually costs.
Code a nearest-neighbour classifier, evaluate it by leaving one out, then write SMOTE by hand and compare.
Imbalanced learning, resampling, cost-sensitive methods and evaluation that holds up to scrutiny.
Judging AI tools that flag rare events at work, from fraud alerts to fault detection, by their errors as well as their accuracy.
Modern Age Coders has no tie to MidCoast Council, the NSW education department or the ABS. Those bodies own the raw rows; the classifiers, the invented training points and each percentage quoted here were produced by scripts we wrote.
A few more details from the department's list and the NSW calendar.
Cundletown Public School records a first teacher in July 1857, the earliest date among the 42 Mid-Coast schools.
Two campuses in Tuncurry share the latest first-teacher date, 29 January 2003: Great Lakes College Tuncurry Campus and the Great Lakes College Senior Campus.
NSW Eastern division schools begin Term 1 for students on Wednesday 3 February 2027.
Spread across the council area
Taree has six schools in the file and Tuncurry and Wingham three each, with Forster and Gloucester two each. The 8 schools teaching secondary years are listed in Taree and Tuncurry (two each), Forster, Wingham, Gloucester and Bulahdelah. Location gives the model something to learn from, yet every one of those towns except Bulahdelah also has a primary school in the file, which is exactly why neighbours alone get confused.
The ages are suggestions only; the free trial decides where each learner starts.
Python that reads a real file, groups and counts, and draws points on a simple chart.
Python for TeensMaths Through CodingNearest neighbours, fair testing and measuring what a model misses, on the Mid-Coast schools and other open data.
AI and Machine Learning for TeensData Science for TeensImbalanced data, resampling, metrics and the statistics behind a fair comparison.
AI and ML MasterclassData Structures & Algorithms CourseQuestioning the models and dashboards that appear at work by their false alarms and misses.
Data Science CourseData Analysis CourseBecause the most useful AI systems hunt for rare things, and that is exactly where impressive-sounding numbers mislead.
Much of practical AI is about finding needles: unusual transactions, faulty parts, rare conditions. A model can be accurate on paper while missing most of them, as ours did by finding 3 secondary schools out of 8 while scoring 76%. Fixes such as SMOTE help it find more, but by learning from invented examples, which brings false alarms.
We teach learners to see all of that: to compare against doing nothing, to count misses and false alarms separately, to rerun with different seeds, and to keep synthetic data away from the test set. Those habits are what separate a model someone can trust from one that only looks good in a demonstration.
In 2026 anyone can train a model in minutes, and a Taree learner who knows how to read its mistakes is the one people will trust with real decisions. The longer argument is in why teenagers should still learn to code in 2026.
The schools in our project lie in towns spread across the Mid-Coast council area, and a lesson on video joins them all together.
A teenager in Wingham and an adult in Forster can work on the same model in the same lesson, with nobody on the road.
Lesson plans track the stage a learner is in and the NSW term calendar, and Year 11 or 12 students can link their AI work to whichever computing or mathematics courses they have chosen.
The trial is a full class with real code, after which we suggest a level, course and weekly time; no card details are requested.
Fifteen to twenty learners at one level, from the Mid-Coast and beyond, so groups are always large enough.
A regular pair of weekly sessions, roughly eight per month, with gaps for exams and school holidays agreed well ahead.
A 5:30pm Taree lesson in winter starts at 1pm in India; when daylight saving begins in New South Wales, it starts at 12pm there.
One clock change each way
New South Wales moves its clocks forward on Sunday 4 October 2026 and back on Sunday 4 April 2027, while India keeps the same time. Lesson times are always set in Taree time, so families never need to work out the difference themselves.
Three clear figures.
One complete lesson at no charge, with a recommended level, course and time to follow.
About eight lessons a month alongside fifteen to twenty learners at the same level.
About eight lessons a month with a teacher, one to one.
Families outside India are billed in US dollars, which means Tuncurry, Wingham and central Taree all see one identical figure. No payment is taken before the trial settles a course and a weekly slot; holidays, missed classes and switching between group and private are covered on the pricing page.
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An age and an interest are enough for a first plan. A Year 9 student might begin by plotting the Mid-Coast schools in Python; an adult might move straight to the full imbalance experiment.
What Taree learners and families tend to ask us first.
The Taree Significant Urban Area had 26,519 residents at the 2021 Census, according to QuickStats, with a median age of 46 compared with 39 for New South Wales.
SMOTE stands for Synthetic Minority Oversampling Technique. It creates new, invented examples of a rare class by placing points between real examples and their nearest neighbours of the same class, so a model sees the rare class more often during training.
Learners train a nearest-neighbour model to pick out the 8 Mid-Coast public schools that teach secondary years from the 42 in the council area. The plain model finds 3 of the 8; with SMOTE it finds about 6, at the cost of many more false alarms.
No. The model uses only founding years and coordinates, as an exercise in imbalanced classification. We draw no conclusion about how well any of the 42 schools teaches, or about who attends them.
Yes. Year 11 and 12 students studying computing or maths in New South Wales have lessons aligned to their stage and terms. Teachers explain techniques and check understanding, while anything submitted for marks stays entirely the learner's work.
Times are agreed at the trial and quoted in Taree time, usually after school or at the weekend. India is 4.5 hours behind Taree in winter and 5.5 hours behind during NSW daylight saving.
Yes, from age six. Younger children begin with Scratch and visual projects; the machine learning project on this page is for teenagers and adults.
No, and none is claimed. All lessons are live and online and we have no premises anywhere in Australia. A learner needs a laptop or desktop, working sound and a reliable connection; the contact number shown is Indian.
The first lesson is free. After that a group place is USD 100 a month for two live lessons a week, around eight a month, with fifteen to twenty learners, or USD 150 a month for the same pattern one to one. Course, format and time are settled before any payment.
Fifteen to twenty learners, grouped by level, pace and goals rather than by age or postcode. When no group suits the time a learner needs, one-to-one lessons are offered instead.
Nearby pages include vibe coding and AI agents classes in Port Macquarie and online coding and Python classes in Nelson Bay. The New South Wales page covers the state, and every other place and topic is listed on the Australia hub.