Artificial Intelligence with Scratch (Mblocks)

<ol start="1"><li><p>Core AI/ML concepts in plain terms — supervised vs unsupervised learning, features, labels, training vs inference, overfitting/underfitting — using block-based visuals.</p></li><li><p>Data collection and annotation — gathering images, sounds, or sensor readings, labeling examples, and understanding dataset quality and bias.</p></li><li><p>Building and training simple models — using mBlock/Teachable Machine-style blocks to train image/sound/classification models and adjust parameters (epochs, examples).</p></li></ol>