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AI Fundamentals

40 hoursBeginner10 modules

8 of 10 modules with a published syllabus

0 of 10 modules recorded

Syllabus

Covers the entire level-100 shared trunk: what AI, ML and DL are; Python for AI; essential mathematics I; data collection and cleaning; basic statistics and probability; a first scikit-learn model; ethics, bias and safety; and Git/GitHub as a portfolio. It closes with the two orientation modules that open the other areas — visual intuition for neural networks, and the systems view of how AI becomes a product.

Modules of this programme

The same objects as the full curriculum. A module opened from any programme that contains it is the same module, with the same syllabus and the same status.

  • CORE-101Syllabus published

    What AI, ML and DL are

    Content
    History, types of learning, where AI already is in the real world
    Hands-on
    Map 5 everyday AI systems and classify them

    estimated price0 of 1 module published

  • CORE-102Syllabus published

    Python for AI

    Content
    Syntax, functions, environments (venv/conda), notebooks
    Hands-on
    Mini-project: a simple text analyser

    estimated price0 of 1 module published

  • CORE-103Syllabus published

    Essential mathematics I

    Content
    Vectors, matrices, functions, the idea of a derivative — visual and intuitive
    Hands-on
    Implement the operations with NumPy

    estimated price0 of 1 module published

  • CORE-104Syllabus published

    Data: collection and cleaning

    Content
    Pandas, CSV/JSON/APIs, missing data, EDA
    Hands-on
    Clean and explore a real, messy dataset

    estimated price0 of 1 module published

  • CORE-105Syllabus published

    Basic statistics and probability

    Content
    Distributions, mean/variance, correlation vs. causation
    Hands-on
    Statistical analysis of a public dataset

    estimated price0 of 1 module published

  • CORE-106Syllabus published

    Your first ML model

    Content
    Scikit-learn: regression and classification, train/test split, metrics
    Hands-on
    Predict housing prices + classify spam

    estimated price0 of 1 module published

  • CORE-107Syllabus published

    Ethics, bias and safety in AI

    Content
    Bias in data, privacy, responsible use
    Hands-on
    Audit an off-the-shelf model for bias

    estimated price0 of 1 module published

  • CORE-108Syllabus published

    Git, GitHub and your portfolio

    Content
    Version control, README, publishing projects
    Hands-on
    Push every project from this level to GitHub

    estimated price0 of 1 module published

  • DL-101Syllabus published

    Visual intuition for neural networks

    Detailed syllabus not written yet

  • MLOPS-101Syllabus published

    How AI becomes a product (systems view)

    Detailed syllabus not written yet

Not everything fits into 40 hours. State of the art, distributed training, interpretability, edge deployment and the five integrative projects exist only inside the complete tracks: Tracks