Your first ML model, end to end
Take this if you can follow a tutorial to 95% accuracy and have no idea whether that number is good.
What you’ll be able to do
You can train a classifier, choose a metric that suits the problem rather than flattering the model, read a confusion matrix, and write a failure analysis that names what the model cannot do.
What it assumes
Basic Python — variables, lists, functions and a loop. No prior machine learning.
- Price
- ₹399
- Length
- 4 weeks
- Sessions
- 12
- Effort
- ~5 h/week · 20 h total
- For
- Class 11–12 · College · Working professional
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What’s taught, in order
- 01
What a model is, minimally
A function with parameters, fitted to data, used to predict. Building the smallest possible version by hand first means nothing later is hidden inside a library call you never opened.
- 02
Fitting, and the split that keeps you honest
Training and test sets, and the specific way a model that has seen its test data lies to you. Leaking data on purpose once is the fastest way to never do it again by accident.
- 03
Metrics that suit the question
Accuracy, precision, recall and the confusion matrix, taught on a deliberately imbalanced problem where accuracy looks excellent and the model is useless.
- 04
Failure analysis
Sorting your model's mistakes into groups and finding the pattern. This is the week that separates someone who ran a tutorial from someone who can be trusted with a model.
What you build
Train a classifier on a dataset you pick, then write a one-page report containing the metric you chose and why, the confusion matrix, the three most common kinds of mistake it makes, and one concrete change you would try next with your reason for expecting it to help.
Check it yourself against this
- Your metric choice is justified by the shape of the problem, not by which number was highest.
- Your confusion matrix is interpreted in words, not merely pasted in.
- You identified at least three distinct error patterns by actually looking at wrong predictions.
- Your proposed next change has a stated reason and a prediction of its effect.
- You can state one thing your model should never be used for.
Then answer this
Your model is 95% accurate and still unusable for its intended purpose — what would have to be true of the data for both those things to hold?
Two or three sentences, in your own words. If you cannot, the course is not finished — go back to the module it came from.
What this course does not do
One model family, one dataset, no hyperparameter search, no cross-validation and no deployment. Ensembles, boosting, feature engineering at scale and the rest of classical machine learning are the phase course, not this sprint.
Where this leads
This sprint is the honest first quarter of the twelve-week Classical Machine Learning phase, which goes on through trees, ensembles, boosting, clustering and the feature engineering that decides most real outcomes.
FAQ
- Which library does this use?
- scikit-learn for the fitting, after you have implemented the simplest model by hand first. That order is deliberate: the library is a convenience once you know what it is doing, and a black box if you meet it first.
- Can I use my own dataset?
- Yes, and it is encouraged for the project — a dataset you care about makes the failure analysis genuinely interesting. There is guidance on what makes a dataset workable, since a first project can be sunk by data that is too small or too clean.
- Is deep learning covered?
- No, deliberately. Classical models are faster to train, easier to inspect and better suited to most real tabular problems, and the habits this sprint builds around evaluation transfer directly when you do move to neural networks.
Other courses
This is a standalone course. It does not add weeks to the 78-week curriculum or change your roadmap. If you want the whole path instead, the full programme is ₹399/month · ₹2,000/year — see what it covers.