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AI & Machine Learningintermediate
AI-201

Machine Learning

When you finish, you can

Build, evaluate and explain a working machine-learning model on a real dataset, from a first regression to a deployed ensemble.

Start hereYou need AI-101 or equivalent and a free Python environment (Google Colab works). Every chapter ends with code you run.

9 hours total5 Chapters20 Lessons1 Exams3 CreditsFREE
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Debate a counselor

Take a position on Machine Learning. The AI counselor argues the other side, asks you questions back, and cites a source for every fact it states. Sources listed under a reply are the pages it actually found; if none are listed, it is arguing from reasoning only. It is an AI and it can be wrong: check the sources.

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    A room for people taking this course, in any language, translated for every reader. Open breakout rooms for one question at a time.

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    Prerequisites::AI-101 Foundations of AI

    Course Syllabus

    9h total

    Practice

    1. Load a small public dataset (house prices or car mileage) in Colab.
    2. Fit a linear regression on one feature, then on three. Print the coefficients.
    3. Plot predictions against actuals and note where the line is worst.
    4. Create one new feature (price per square foot) and refit.

    Check

    Which added feature helped most, and how do you know it was not luck?

    Topics Covered

    mlregressionclassification

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