---
title: "AI102 · How machines learn"
publisher: Maestro College (Maestro Open)
lessons: 35
source: https://open.maestrocollege.edu/how-machines-learn
full: https://open.maestrocollege.edu/how-machines-learn/llms-full.txt
---

# How machines learn (AI102)

See problems as machine-learning problems, understand your data, and train and evaluate your first supervised models.

## Course objectives
- Determine whether a problem is appropriate for machine learning and select suitable supervised or unsupervised approaches.
- Prepare and explore datasets while identifying data-quality, leakage, imbalance, and bias concerns that affect model use.
- Train baseline and machine-learning models using reproducible data-preparation and evaluation workflows.
- Interpret regression, classification, and clustering results using measures appropriate to the task and consequences of errors.
- Evaluate model generalization, diagnose fitting problems, and make evidence-based recommendations about model readiness.

## Course teaching approach
- Start from a real-world problem, then introduce the concept as the solution.
- Keep it true beginner level, hands-on, a little gamified. The learner should feel they are winning.
- Balance explanation, demonstration and practice. No copy-paste exercises: give small story-based tasks.
- End with one fun closed question about the topic.

## Lessons
### Unit 1: Seeing problems as ML problems
- [From hand-written rules to learning](https://open.maestrocollege.edu/how-machines-learn/from-hand-written-rules-to-learning.md)
- [Where ML fits in the AI map](https://open.maestrocollege.edu/how-machines-learn/where-ml-fits-in-the-ai-map.md)
- [Deep learning, generative AI, and LLMs](https://open.maestrocollege.edu/how-machines-learn/deep-learning-generative-ai-and-llms.md)
- [Features, labels, and examples](https://open.maestrocollege.edu/how-machines-learn/features-labels-and-examples.md)
- [Is this an ML problem?](https://open.maestrocollege.edu/how-machines-learn/is-this-an-ml-problem.md)
- [Regression, classification, and clustering](https://open.maestrocollege.edu/how-machines-learn/regression-classification-and-clustering.md)
- [The ML workflow loop](https://open.maestrocollege.edu/how-machines-learn/the-ml-workflow-loop.md)
- [Setting up your ML workspace in VS Code](https://open.maestrocollege.edu/how-machines-learn/setting-up-your-ml-workspace-in-vs-code.md)
- [Running your first model template](https://open.maestrocollege.edu/how-machines-learn/running-your-first-model-template.md)
- [Challenge: framing ML problems](https://open.maestrocollege.edu/how-machines-learn/challenge-framing-ml-problems.md)

### Unit 2: Understanding data before modeling
- [Collecting and loading a dataset](https://open.maestrocollege.edu/how-machines-learn/collecting-and-loading-a-dataset.md)
- [Exploring data with pandas](https://open.maestrocollege.edu/how-machines-learn/exploring-data-with-pandas.md)
- [Visualizing distributions and relationships](https://open.maestrocollege.edu/how-machines-learn/visualizing-distributions-and-relationships.md)
- [Handling missing values and outliers](https://open.maestrocollege.edu/how-machines-learn/handling-missing-values-and-outliers.md)
- [Spotting imbalanced and biased data](https://open.maestrocollege.edu/how-machines-learn/spotting-imbalanced-and-biased-data.md)
- [Encoding categories and scaling features](https://open.maestrocollege.edu/how-machines-learn/encoding-categories-and-scaling-features.md)
- [Creating useful features](https://open.maestrocollege.edu/how-machines-learn/creating-useful-features.md)
- [Splitting data into train and test sets](https://open.maestrocollege.edu/how-machines-learn/splitting-data-into-train-and-test-sets.md)
- [Preparing data with a pipeline](https://open.maestrocollege.edu/how-machines-learn/preparing-data-with-a-pipeline.md)
- [Challenge: data readiness check](https://open.maestrocollege.edu/how-machines-learn/challenge-data-readiness-check.md)

### Unit 3: Training and evaluating supervised models
- [Training a linear regression model](https://open.maestrocollege.edu/how-machines-learn/training-a-linear-regression-model.md)
- [Measuring regression error](https://open.maestrocollege.edu/how-machines-learn/measuring-regression-error.md)
- [Beating a simple baseline](https://open.maestrocollege.edu/how-machines-learn/beating-a-simple-baseline.md)
- [Classifying with logistic regression](https://open.maestrocollege.edu/how-machines-learn/classifying-with-logistic-regression.md)
- [Classifying with decision trees](https://open.maestrocollege.edu/how-machines-learn/classifying-with-decision-trees.md)
- [Accuracy and the confusion matrix](https://open.maestrocollege.edu/how-machines-learn/accuracy-and-the-confusion-matrix.md)
- [Precision, recall, and the cost of errors](https://open.maestrocollege.edu/how-machines-learn/precision-recall-and-the-cost-of-errors.md)
- [Challenge: compare two classifiers](https://open.maestrocollege.edu/how-machines-learn/challenge-compare-two-classifiers.md)
- [Diagnosing overfitting and underfitting](https://open.maestrocollege.edu/how-machines-learn/diagnosing-overfitting-and-underfitting.md)
- [Cross-validation basics](https://open.maestrocollege.edu/how-machines-learn/cross-validation-basics.md)

### Unit 4: Clustering and the full workflow
- [Finding groups with k-means clustering](https://open.maestrocollege.edu/how-machines-learn/finding-groups-with-k-means-clustering.md)
- [Choosing and judging clusters](https://open.maestrocollege.edu/how-machines-learn/choosing-and-judging-clusters.md)
- [Classic ML vs. deep learning in practice](https://open.maestrocollege.edu/how-machines-learn/classic-ml-vs-deep-learning-in-practice.md)
- [Challenge: end-to-end ML workflow](https://open.maestrocollege.edu/how-machines-learn/challenge-end-to-end-ml-workflow.md)
- [Challenge: is this model good enough?](https://open.maestrocollege.edu/how-machines-learn/challenge-is-this-model-good-enough.md)

## How to use this document
You are acting as a tutor for a learner on Maestro Open. Teach from this document, one small step at a time. Ask the learner to try things, wait for their answer, then give feedback. Keep it beginner-friendly. If the lesson involves code or spreadsheets and you cannot run them, ask the learner to try it on their own computer and paste the result back. Do not award grades, credit or certificates.
Start with lesson 1 unless the learner asks for a specific lesson. Keep track of which lesson you are on.

## When a lesson is done
Teach one lesson at a time. A lesson is complete only when the learner has shown they can do every outcome listed for it; check each with a small task or question.
When it is complete:
1. Say clearly: "Lesson N complete: <lesson title>." (use the lesson's number and title).
2. Give a two-line recap of what they learned.
3. Name the next lesson, say "Lesson N+1 of 35: <title>", and ask if they're ready to start it. Start it only when they say yes.
After the last lesson, congratulate them on finishing the course.