A professional AI learning platform showcasing Machine Learning, Deep Learning, NLP, and Agentic AI.
From theory to production — built on Synology NAS, deployed to AWS.
Each project is a containerized microservice — built and tested on NAS, deployed to AWS.
K-Nearest Neighbors classifier on the classic Iris dataset. Submit features, get classification with confidence scores.
/ml/iris-knn/ Try it →Linear Regression on the California Housing dataset. Predict median house values from 8 features with an interactive slider UI.
/ml/housing-linear/ Try it →PyCaret AutoML pipeline that benchmarks 7 algorithms on the Titanic survival dataset and serves the best model with AUC/F1/Accuracy metrics.
/ml/titanic-automl/ Try it →PyTorch CNN classifier on MNIST. Draw a digit on the HTML5 canvas, get the prediction with per-class confidence bars. ≥98% test accuracy.
/dl/mnist-cnn/ Try it →PyTorch LSTM trained on 2.5 years of NYC CitiBike daily ride counts. Pick a date, get a 14-day forecast with an MC-Dropout confidence band and a compare-to-actuals overlay.
/dl/lstm-forecast/ Try it →Real-time object detection — bounding boxes and class labels via API.
/dl/object-detection PlannedAre two Quora questions asking the same thing? Bag-of-words + 22 engineered features (token, length, fuzzy) → Random Forest, with a probability slider and a “why” panel.
/nlp/quora-randomforest/ PlannedPaste any text and watch every step — cleaning, tokenization, stemming vs. lemmatization — then compare one-hot, Bag of Words, n-grams and TF-IDF vectors side by side.
Phase 3b PlannedWord embeddings trained on a custom corpus: nearest words, king − man + woman analogies, and a 3D PCA map of the vocabulary.
Phase 3c PlannedA hand-built Hidden Markov Model tags each word, with the Viterbi path drawn step by step — side by side with spaCy's neural tagger.
Phase 3d PlannedAutonomous AI agent powered by Claude API. Multi-step reasoning, tool use, and structured outputs.
/agentic/claude-agent PlannedProduction-grade tools across all domains.
Shared services powering all ML experiments and model tracking.
Experiment tracking, run history, parameters, metrics, and model artifact storage for all ML projects.
Open MLflow →S3-compatible object storage for model artifacts, datasets, and MLflow experiment outputs.
ml-minio:9000 · internalShared relational database for prediction audit trails and structured ML project data.
ml-postgres:5432 · internalIn-memory cache for inference results, rate limiting, and session state across ML services.
ml-redis:6379 · internal