Skip to content

Learning Path

Path A: New to Applied ML

  1. projects/deep-learning-math-foundations-showcase
  2. projects/sota-supervised-learning-showcase
  3. projects/sota-unsupervised-semisup-showcase
  4. projects/causalml-kaggle-showcase

Path B: Deep Learning Foundations

  1. projects/deep-learning-math-foundations-showcase
  2. projects/neural-network-foundations-showcase
  3. projects/pytorch-training-regularization-showcase
  4. projects/sota-supervised-learning-showcase

Path C: Decision Science / Causal Focus

  1. projects/sota-supervised-learning-showcase
  2. projects/causalml-kaggle-showcase
  3. projects/xai-fairness-audit-showcase
  4. projects/mlops-drift-production-showcase

Path D: ML in Production Focus

  1. projects/sota-supervised-learning-showcase
  2. projects/mlops-drift-production-showcase
  3. projects/batch-vs-stream-ml-systems-showcase
  4. projects/model-release-rollout-showcase

Path E: Modeling Optimization Focus

  1. projects/sota-supervised-learning-showcase
  2. projects/automl-hpo-showcase
  3. projects/autoresearch
  4. projects/rl-bandits-policy-showcase

Path F: Feature and Representation Focus

  1. projects/sota-supervised-learning-showcase
  2. projects/feature-engineering-dimred-showcase
  3. projects/sota-unsupervised-semisup-showcase

Path G: Short Course (Two Weeks)

  • Day 1-2: deep-learning-math-foundations-showcase
  • Day 3-4: neural-network-foundations-showcase
  • Day 5-6: pytorch-training-regularization-showcase
  • Day 7-8: sota-supervised-learning-showcase
  • Day 9-10: feature-engineering-dimred-showcase
  • Day 11-12: xai-fairness-audit-showcase
  • Day 13-14: mlops-drift-production-showcase

Path M: Deep Learning Mini-Series

  1. projects/deep-learning-math-foundations-showcase
  2. projects/neural-network-foundations-showcase
  3. projects/pytorch-training-regularization-showcase
  4. projects/sota-unsupervised-semisup-showcase

Path H: Contract-First Supervised Workflow

  1. projects/eda-leakage-profiling-showcase
  2. projects/feature-engineering-dimred-showcase
  3. projects/automl-hpo-showcase
  4. projects/xai-fairness-audit-showcase

Path I: Credit Risk Capstone Workflow

  1. projects/eda-leakage-profiling-showcase
  2. projects/feature-engineering-dimred-showcase
  3. projects/credit-risk-classification-capstone-showcase
  4. projects/xai-fairness-audit-showcase

Path J: Ranking and Serving Workflow

  1. projects/learning-to-rank-foundations-showcase
  2. projects/ranking-api-productization-showcase
  3. projects/model-release-rollout-showcase

Path K: Forecasting and Observability Workflow

  1. projects/nyc-demand-forecasting-foundations-showcase
  2. projects/demand-api-observability-showcase
  3. projects/mlops-drift-production-showcase

Path L: Agentic Research Workflow

  1. projects/automl-hpo-showcase
  2. projects/autoresearch
  3. projects/model-release-rollout-showcase

How To Know You Are Progressing

  • You can explain outputs in plain language.
  • You can justify model choices with evidence.
  • You can describe one limitation or risk per method.
  • You can propose a production or governance guardrail for each modeling workflow.

Coverage Cross-Reference

Use docs/aspect-coverage-matrix.md to confirm which project demonstrates each method (splits, imbalance handling, explainability, HPO, tracking, and productionization).

Track Pages

For track-level documentation with artifact-focused guidance:

  • docs/tracks/foundations.md
  • docs/tracks/production.md
  • docs/tracks/ranking.md
  • docs/tracks/forecasting.md
  • docs/tracks/responsible-ai.md
  • docs/tracks/optimization.md