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Level 2 · Intermediate Applied ML

Goal: broaden from the Level 1 foundation into the models and domains used most in applied ML work — boosted trees, time series, text, images — and learn to explain and honestly evaluate what you build.

Modules

  1. Ensemble Methods Deep Dive
  2. Gradient Boosting & XGBoost
  3. Time Series Forecasting Basics
  4. NLP Basics — Text Features & Classification
  5. CNNs for Image Classification
  6. Transfer Learning Basics
  7. Model Interpretability
  8. Imbalanced Data & Advanced Evaluation
  9. Recommender System Fundamentals
  10. Project — End-to-End Modeling Workflow