44 entries,
8 categories.
What patterns emerge when text becomes numbers? Explore data and ML concepts from text analysis to model evaluation through live demos.
What’s inside
Explore each concept in a live demo. Open a card for the explanation and code.
Browse by field
Text to numbers
Finding structure
Training models
TText representation
Turn writing into computable values with analysis pipelines, tokenization, morphological analysis, and TF-IDF.
vEmbeddings & similarity
Explore semantic distance with sentence embeddings, cosine similarity, centroids, and nearest-neighbor search.
↘Dimensionality reduction
View high-dimensional relationships in fewer dimensions with dimensionality reduction, PCA, UMAP, and t-SNE.
∴Clustering
Group and interpret similar data with K-means, DBSCAN, HDBSCAN, and cluster keywords.
μDistributions & statistics
Read the center and spread of values through medians, interquartile ranges, long-tail distributions, and Z-scores.
fModel types
Compare prediction methods including linear regression, decision trees, random forests, and neural networks.
∇Training & optimization
See how models adjust values through feature engineering, loss functions, gradient descent, and learning rates.
✓Validation & evaluation
Check generalization with train-test splits, cross-validation, confusion matrices, and precision-recall.