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Level 1 · Entry Foundations

Goal: get comfortable with the scientific Python stack, train and honestly evaluate classical ML models with scikit-learn, understand the fundamentals of neural networks with PyTorch, and ship a complete end-to-end tabular ML project.

Modules

  1. Setup & the Scientific Python Stack
  2. NumPy & Pandas Essentials
  3. Data Preparation
  4. Regression
  5. Classification
  6. Clustering & Unsupervised Learning
  7. Model Evaluation & Cross-Validation
  8. Feature Engineering & Pipelines
  9. Neural Network Fundamentals with PyTorch
  10. Capstone — End-to-End Tabular ML Project

By the end of this level you'll be able to take a raw tabular dataset, prepare it with a leakage-proof pipeline, train and compare several models, evaluate them with cross-validation, and save the winner for reuse — plus train your first neural network from scratch in PyTorch.