MICRO-COURSE

Machine Learning

Machine Learning: 13 short lessons, one for each concept. Supervised learning, unsupervised learning, semi-supervised learning, regression, classification, clustering, dimensionality reduction, feature engineering, cross-validation, regularization, overfitting, data leakage, and model selection.

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01 / WATCH

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02 / UNDERSTAND

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Supervised learning, unsupervised learning, semi-supervised learning, regression, classification, clustering, dimensionality reduction, feature engineering, cross-validation, regularization, overfitting, data leakage, and model selection.

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Concept scope

03 / TRY IT

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Choose one question about Machine Learning that you want to answer.
Write down a small example, its inputs or starting point, and the result you expect.
Explain your reasoning and note what you would like reviewed in a live or 1:1 session.

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A short example, reasoning and an open question

Check your understanding

Explain the concept in your own words and identify where it fits in the subject.
Show one small example, the expected result and the reasoning behind it.
Record one limitation or edge case and a question to discuss with a mentor.

04 / LEARN WITH HELP

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CONTINUE WITH ONE CONCEPT

Explore 13 focused topics.

Each topic opens its own micro-course, with its learning guide, recording status and session requests.

13 results1–12

Lesson

Supervised learning

A short lesson on supervised learning from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Unsupervised learning

A short lesson on unsupervised learning from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Semi-supervised learning

A short lesson on semi-supervised learning from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Regression

A short lesson on regression from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Classification

A short lesson on classification from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Clustering

A short lesson on clustering from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Dimensionality reduction

A short lesson on dimensionality reduction from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Feature engineering

A short lesson on feature engineering from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Cross-validation

A short lesson on cross-validation from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Regularization

A short lesson on regularization from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Overfitting

A short lesson on overfitting from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Data leakage

A short lesson on data leakage from the Machine Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Machine Learning

Recorded video

Start with the topic guide.

Concept scope

Prepare a focused example

Check your understanding

Need a walkthrough?Stay with this topic.

Request a live session for this topic.

Explore 13 focused topics.

Supervised learning

Unsupervised learning

Semi-supervised learning

Regression

Classification

Clustering

Dimensionality reduction

Feature engineering

Cross-validation

Regularization

Overfitting

Data leakage

Need a walkthrough?
Stay with this topic.