{"locale":"en","sections":[{"id":"data-foundations","title":"Data Foundations","summary":"Python, SQL, storage formats, ETL, and the data contracts that make ML work reproducible.","modules":[{"slug":"python-for-ml","sectionId":"data-foundations","title":"Python for ML","summary":"Python language and project patterns that keep ML code testable, typed, logged, and production-ready.","focus":["typing and dataclasses","generators and decorators","async basics","GIL, multiprocessing, and tests"],"featureIdea":"Notebook-to-production refactor playground with typing and pytest failures.","status":"shell","requiresBackend":false,"tags":["python","testing","production"],"locale":"en","sectionTitle":"Data Foundations","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/python-for-ml","apiPath":"/api/learning/python-for-ml"},{"slug":"sql-databases","sectionId":"data-foundations","title":"SQL and databases","summary":"Relational querying, indexing, transactions, OLTP/OLAP 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now","pagePath":"/learn/linear-algebra","apiPath":"/api/learning/linear-algebra"},{"slug":"calculus-optimization","sectionId":"math-for-ml","title":"Calculus and optimization","summary":"Derivatives, gradients, chain rule, SGD, Adam, saddle points, clipping, and unstable learning rates.","focus":["gradient descent","Adam vs SGD","backpropagation","gradient clipping"],"featureIdea":"Gradient descent playground with learning rate, momentum, Adam, clipping, and saddle points.","status":"shell","requiresBackend":false,"tags":["optimization","gradients","adam"],"locale":"en","sectionTitle":"Math for ML","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/calculus-optimization","apiPath":"/api/learning/calculus-optimization"},{"slug":"probability-statistics","sectionId":"math-for-ml","title":"Probability and statistics","summary":"Random variables, distributions, Bayes, MLE/MAP, confidence intervals, bootstrap, Monte Carlo, and uncertainty.","focus":["likelihood vs 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imbalance, and distribution mismatch.","status":"shell","requiresBackend":true,"tags":["eda","profiling","leakage"],"locale":"en","sectionTitle":"Data Preparation","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/eda","apiPath":"/api/learning/eda"},{"slug":"data-cleaning","sectionId":"data-preparation","title":"Data cleaning","summary":"Missing-value strategies, outlier handling, clipping, denoising, scaling, log transforms, and rare categories.","focus":["imputation","outliers","normalization","rare category handling"],"featureIdea":"Missing-value and outlier strategy lab with before/after metric changes.","status":"shell","requiresBackend":true,"tags":["cleaning","imputation","outliers"],"locale":"en","sectionTitle":"Data Preparation","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/data-cleaning","apiPath":"/api/learning/data-cleaning"},{"slug":"feature-engineering","sectionId":"data-preparation","title":"Feature 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now","pagePath":"/learn/regression","apiPath":"/api/learning/regression"},{"slug":"classification","sectionId":"classical-ml","title":"Classification metrics","summary":"Confusion matrix, accuracy, precision, recall, F1, ROC-AUC, PR-AUC, thresholds, calibration, and imbalance.","focus":["threshold tuning","precision vs recall","ROC vs PR","calibration"],"featureIdea":"Threshold tuner for precision, recall, F1, ROC-AUC, PR-AUC, and calibration.","status":"shell","requiresBackend":false,"tags":["classification","metrics","thresholds"],"locale":"en","sectionTitle":"Classical Machine Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/classification","apiPath":"/api/learning/classification"},{"slug":"tree-models","sectionId":"classical-ml","title":"Tree-based models","summary":"Decision trees, entropy, Gini, random forests, bagging, boosting, XGBoost, LightGBM, and CatBoost.","focus":["split criteria","bagging vs boosting","feature importance","categorical 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transformers.","modules":[{"slug":"neural-network-foundations","sectionId":"deep-learning","title":"Neural network foundations","summary":"Perceptrons, MLPs, computational graphs, backprop, activations, initialization, optimizers, and regularization.","focus":["forward and backward pass","activation functions","dropout and weight decay","AdamW"],"featureIdea":"Forward/backprop visualizer with activations, loss, dropout, initialization, and optimizer choices.","status":"shell","requiresBackend":false,"tags":["neural-networks","backprop","optimizers"],"locale":"en","sectionTitle":"Deep Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/neural-network-foundations","apiPath":"/api/learning/neural-network-foundations"},{"slug":"deep-learning-architectures","sectionId":"deep-learning","title":"Deep learning architectures","summary":"Feedforward nets, autoencoders, VAEs, CNNs, RNNs, Seq2Seq, attention, transformers, GANs, and diffusion.","focus":["architecture families","metric learning","generative models","when to use each"],"featureIdea":"Architecture map comparing MLP, autoencoder, CNN, RNN, Transformer, GAN, and diffusion.","status":"shell","requiresBackend":false,"tags":["architectures","cnn","transformer"],"locale":"en","sectionTitle":"Deep Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/deep-learning-architectures","apiPath":"/api/learning/deep-learning-architectures"},{"slug":"cnn","sectionId":"deep-learning","title":"CNN","summary":"Convolution, kernels, stride, padding, pooling, receptive field, channels, ResNet, augmentation, and transfer learning.","focus":["filters","padding and stride","receptive field","skip connections"],"featureIdea":"Convolution kernel explorer with stride, padding, receptive field, pooling, and transfer learning.","status":"shell","requiresBackend":false,"tags":["cnn","vision","convolution"],"locale":"en","sectionTitle":"Deep 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cache.","focus":["scaled dot-product attention","multi-head attention","causal masks","KV cache"],"featureIdea":"Self-attention playground showing tokens, Q/K/V, masks, heads, positional encoding, and KV cache.","status":"shell","requiresBackend":false,"tags":["transformers","attention","llm"],"locale":"en","sectionTitle":"Deep Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/transformers","apiPath":"/api/learning/transformers"}]},{"id":"nlp","title":"NLP","summary":"Classical NLP through modern LLMs, tokenization, embeddings, RAG, agents, and audio NLP.","modules":[{"slug":"classical-nlp","sectionId":"nlp","title":"Classical NLP","summary":"Tokenization, normalization, n-grams, BoW, TF-IDF, edit distance, Naive Bayes, topic modeling, NER, POS, and parsing.","focus":["preprocessing","BoW vs TF-IDF","language modeling","topic modeling"],"featureIdea":"Text preprocessing and TF-IDF lab with n-grams, edit distance, Naive Bayes, and topic modeling.","status":"shell","requiresBackend":true,"tags":["nlp","tf-idf","topic-modeling"],"locale":"en","sectionTitle":"NLP","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/classical-nlp","apiPath":"/api/learning/classical-nlp"},{"slug":"word-embeddings","sectionId":"nlp","title":"Word embeddings","summary":"Distributional hypothesis, Word2Vec, CBOW, Skip-gram, negative sampling, GloVe, FastText, subwords, and OOV.","focus":["CBOW vs Skip-gram","negative sampling","subword embeddings","analogies"],"featureIdea":"Word2Vec/FastText embedding space with analogy and out-of-vocabulary examples.","status":"shell","requiresBackend":true,"tags":["embeddings","word2vec","fasttext"],"locale":"en","sectionTitle":"NLP","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/word-embeddings","apiPath":"/api/learning/word-embeddings"},{"slug":"deep-nlp","sectionId":"nlp","title":"Deep NLP","summary":"RNN language models, Seq2Seq, transformer LMs, MLM/CLM, classification, token tagging, QA, summarization, translation, and retrieval QA.","focus":["language modeling","sequence classification","token classification","semantic search"],"featureIdea":"Task board for language modeling, sequence classification, QA, summarization, translation, and semantic search.","status":"shell","requiresBackend":true,"tags":["deep-nlp","qa","semantic-search"],"locale":"en","sectionTitle":"NLP","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/deep-nlp","apiPath":"/api/learning/deep-nlp"},{"slug":"tokenization","sectionId":"nlp","title":"Tokenization for modern LLMs","summary":"Characters, words, subwords, BPE, WordPiece, Unigram, vocabulary, special tokens, padding, truncation, and masks.","focus":["BPE","WordPiece","special tokens","attention masks"],"featureIdea":"BPE/WordPiece/Unigram tokenizer comparator for Russian and English text plus token cost estimates.","status":"shell","requiresBackend":true,"tags":["tokenization","bpe","llm"],"locale":"en","sectionTitle":"NLP","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/tokenization","apiPath":"/api/learning/tokenization"},{"slug":"llms","sectionId":"nlp","title":"LLMs, RAG, agents, and evals","summary":"Pretraining, fine-tuning, SFT, RLHF, DPO, LoRA, quantization, RAG, vector databases, reranking, tool use, agents, safety, and evals.","focus":["RAG pipeline","fine-tuning vs prompting","reranking","LLM evals"],"featureIdea":"RAG pipeline workbench with chunking, embeddings, reranking, hallucination tests, and cost/latency tradeoffs.","status":"shell","requiresBackend":true,"tags":["llm","rag","agents","evals"],"locale":"en","sectionTitle":"NLP","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/llms","apiPath":"/api/learning/llms"},{"slug":"speech-audio-nlp","sectionId":"nlp","title":"Speech and audio NLP","summary":"Waveforms, sampling rate, Fourier/STFT, spectrograms, Mel scale, MFCC, CTC, ASR, TTS, diarization, VAD, wav2vec, and Whisper-like models.","focus":["spectrograms","MFCC","CTC alignment","ASR and TTS"],"featureIdea":"Waveform-to-spectrogram explorer with sampling rate, MFCC, CTC alignment, ASR, and TTS concepts.","status":"shell","requiresBackend":true,"tags":["audio","asr","spectrogram"],"locale":"en","sectionTitle":"NLP","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/speech-audio-nlp","apiPath":"/api/learning/speech-audio-nlp"}]},{"id":"generative-models","title":"Generative Models","summary":"VAEs, GANs, diffusion, latent generation, conditioning, and image-text alignment.","modules":[{"slug":"generative-models","sectionId":"generative-models","title":"Diffusion and generative models","summary":"Autoencoders, VAEs, GANs, normalizing flows, forward noising, reverse denoising, score matching, U-Net, guidance, latent diffusion, and CLIP.","focus":["GAN vs VAE vs diffusion","noise schedules","classifier-free guidance","latent diffusion"],"featureIdea":"GAN/VAE/diffusion comparison with noising, denoising, guidance, latent diffusion, and CLIP conditioning.","status":"shell","requiresBackend":true,"tags":["diffusion","gan","vae"],"locale":"en","sectionTitle":"Generative Models","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/generative-models","apiPath":"/api/learning/generative-models"}]},{"id":"mlops","title":"MLOps","summary":"Lifecycle, experiments, versioning, deployment, monitoring, LLMOps, and production reliability.","modules":[{"slug":"ml-lifecycle","sectionId":"mlops","title":"ML lifecycle","summary":"Problem framing, data collection, labeling, validation, feature engineering, training, registry, deployment, monitoring, retraining, rollback, and governance.","focus":["model lifecycle","registry","deployment","monitoring and retraining"],"featureIdea":"End-to-end lifecycle board from problem framing and labeling to registry, deployment, monitoring, and retraining.","status":"shell","requiresBackend":true,"tags":["mlops","lifecycle","governance"],"locale":"en","sectionTitle":"MLOps","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/ml-lifecycle","apiPath":"/api/learning/ml-lifecycle"},{"slug":"experiment-management","sectionId":"mlops","title":"Experiment management","summary":"Reproducibility, seeds, deterministic training, experiment tracking, artifacts, model registry, config management, and tuning.","focus":["reproducibility","tracking","artifact storage","hyperparameter tuning"],"featureIdea":"Experiment tracker demo with seeds, configs, metrics, artifacts, and hyperparameter tuning.","status":"shell","requiresBackend":true,"tags":["experiments","mlflow","tracking"],"locale":"en","sectionTitle":"MLOps","statusLabel":"shell","backendLabel":"backend needed 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shadow, A/B, rollback, warmup, batching, and caching.","focus":["serving patterns","rollouts","batching and caching","rollback"],"featureIdea":"Batch vs online vs streaming inference simulator with canary, blue-green, shadow, rollback, caching, and batching.","status":"shell","requiresBackend":true,"tags":["deployment","serving","rollback"],"locale":"en","sectionTitle":"MLOps","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/deployment-patterns","apiPath":"/api/learning/deployment-patterns"},{"slug":"monitoring","sectionId":"mlops","title":"Monitoring","summary":"Service metrics, ML metrics, data drift, concept drift, calibration, LLM cost/tokens/latency/tool success/retrieval quality, alerts, dashboards, logs, traces, and OpenTelemetry.","focus":["latency and errors","data and concept drift","LLM metrics","logs and traces"],"featureIdea":"Service, ML, and LLM metric dashboard with drift, calibration, retrieval quality, traces, and alerts.","status":"shell","requiresBackend":true,"tags":["monitoring","drift","observability"],"locale":"en","sectionTitle":"MLOps","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/monitoring","apiPath":"/api/learning/monitoring"},{"slug":"llmops","sectionId":"mlops","title":"LLMOps","summary":"Prompt versioning, evals, golden datasets, RAG evals, retrieval metrics, traces, tool logs, failure taxonomies, guardrails, moderation, caching, rate limits, fallbacks, cost control, HITL, and synthetic eval generation.","focus":["prompt versioning","RAG evals","agent traces","cost control"],"featureIdea":"Prompt/eval/trace/cost dashboard with golden datasets, RAG evals, guardrails, rate limits, and fallbacks.","status":"shell","requiresBackend":true,"tags":["llmops","evals","guardrails"],"locale":"en","sectionTitle":"MLOps","statusLabel":"shell","backendLabel":"backend needed 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monitoring, CI/CD, model gates, Docker, deployment, LLMOps traces, cost, and failure clustering.","focus":["pipeline stages","monitoring","CI/CD","LLMOps traces"],"featureIdea":"Pipeline, monitoring, CI/CD, and LLMOps experiment board with status and artifact links.","status":"shell","requiresBackend":true,"tags":["mlops","pipeline","ci-cd"],"locale":"en","sectionTitle":"Practical AI Agent Layer","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/mlops-experiments-backlog","apiPath":"/api/learning/mlops-experiments-backlog"}]}],"items":[{"slug":"python-for-ml","sectionId":"data-foundations","title":"Python for ML","summary":"Python language and project patterns that keep ML code testable, typed, logged, and production-ready.","focus":["typing and dataclasses","generators and decorators","async basics","GIL, multiprocessing, and tests"],"featureIdea":"Notebook-to-production refactor playground with typing and pytest 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and pipelines","summary":"CSV, JSONL, Parquet, schema evolution, idempotent pipelines, lineage, backfills, and data quality.","focus":["format tradeoffs","ETL vs ELT","schema drift","late-arriving data"],"featureIdea":"Pipeline simulator showing schema drift, backfills, idempotency, and late data.","status":"shell","requiresBackend":true,"tags":["etl","data-quality","parquet"],"locale":"en","sectionTitle":"Data Foundations","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/data-formats-etl","apiPath":"/api/learning/data-formats-etl"},{"slug":"linear-algebra","sectionId":"math-for-ml","title":"Linear algebra","summary":"Vectors, matrices, tensors, projections, cosine similarity, PCA, SVD, rank, and attention math.","focus":["embeddings","matrix multiplication","PCA and SVD","Q/K/V attention"],"featureIdea":"Embedding geometry board for cosine similarity, projections, PCA, and low-rank 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mismatch.","focus":["data profiling","leakage hints","target imbalance","distribution mismatch"],"featureIdea":"Dataset profiler for missing values, outliers, leakage hints, imbalance, and distribution mismatch.","status":"shell","requiresBackend":true,"tags":["eda","profiling","leakage"],"locale":"en","sectionTitle":"Data Preparation","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/eda","apiPath":"/api/learning/eda"},{"slug":"data-cleaning","sectionId":"data-preparation","title":"Data cleaning","summary":"Missing-value strategies, outlier handling, clipping, denoising, scaling, log transforms, and rare categories.","focus":["imputation","outliers","normalization","rare category handling"],"featureIdea":"Missing-value and outlier strategy lab with before/after metric changes.","status":"shell","requiresBackend":true,"tags":["cleaning","imputation","outliers"],"locale":"en","sectionTitle":"Data Preparation","statusLabel":"shell","backendLabel":"backend 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regularization.","focus":["forward and backward pass","activation functions","dropout and weight decay","AdamW"],"featureIdea":"Forward/backprop visualizer with activations, loss, dropout, initialization, and optimizer choices.","status":"shell","requiresBackend":false,"tags":["neural-networks","backprop","optimizers"],"locale":"en","sectionTitle":"Deep Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/neural-network-foundations","apiPath":"/api/learning/neural-network-foundations"},{"slug":"deep-learning-architectures","sectionId":"deep-learning","title":"Deep learning architectures","summary":"Feedforward nets, autoencoders, VAEs, CNNs, RNNs, Seq2Seq, attention, transformers, GANs, and diffusion.","focus":["architecture families","metric learning","generative models","when to use each"],"featureIdea":"Architecture map comparing MLP, autoencoder, CNN, RNN, Transformer, GAN, and diffusion.","status":"shell","requiresBackend":false,"tags":["architectures","cnn","transformer"],"locale":"en","sectionTitle":"Deep Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/deep-learning-architectures","apiPath":"/api/learning/deep-learning-architectures"},{"slug":"cnn","sectionId":"deep-learning","title":"CNN","summary":"Convolution, kernels, stride, padding, pooling, receptive field, channels, ResNet, augmentation, and transfer learning.","focus":["filters","padding and stride","receptive field","skip connections"],"featureIdea":"Convolution kernel explorer with stride, padding, receptive field, pooling, and transfer learning.","status":"shell","requiresBackend":false,"tags":["cnn","vision","convolution"],"locale":"en","sectionTitle":"Deep Learning","statusLabel":"shell","backendLabel":"static shell now","pagePath":"/learn/cnn","apiPath":"/api/learning/cnn"},{"slug":"sequence-models","sectionId":"deep-learning","title":"RNN and sequence 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and tuning.","focus":["reproducibility","tracking","artifact storage","hyperparameter tuning"],"featureIdea":"Experiment tracker demo with seeds, configs, metrics, artifacts, and hyperparameter tuning.","status":"shell","requiresBackend":true,"tags":["experiments","mlflow","tracking"],"locale":"en","sectionTitle":"MLOps","statusLabel":"shell","backendLabel":"backend needed later","pagePath":"/learn/experiment-management","apiPath":"/api/learning/experiment-management"},{"slug":"data-model-versioning","sectionId":"mlops","title":"Data and model versioning","summary":"Code, datasets, features, offline/online consistency, training-serving skew, schema validation, and data contracts.","focus":["dataset versions","feature stores","schema validation","training-serving skew"],"featureIdea":"Version graph for code, data, features, schemas, training-serving skew, and 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