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Data for AI

40 hoursBeginner9 modules

6 of 9 modules with a published syllabus

0 of 9 modules recorded

Syllabus

Treats data as the object of work before the model: Python, essential mathematics, collection and cleaning with Pandas, statistics and a first supervised model, then the two modules that introduce images and text as data (pixels, channels and OpenCV; tokens, cleaning and regex). It closes with evaluation and experimentation — overfitting, cross-validation, tracking — and with data pipelines, which is where the MLOps area begins. It shares five modules with AI Fundamentals; they are the same object, not a copy.

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-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

  • CV-101Syllabus published

    Images as data (pixels, channels, OpenCV)

    Detailed syllabus not written yet

  • NLP-101Syllabus published

    Text as data (tokens, cleaning, regex)

    Detailed syllabus not written yet

  • CORE-204Syllabus published

    Evaluation and experimentation

    Content
    Overfitting, cross-validation, experiment tracking (W&B/MLflow)
    Hands-on
    Run and compare 10 experiments

    estimated price0 of 1 module published

  • MLOPS-201Syllabus published

    Data pipelines

    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