The big picture

Here’s a realistic “career-grade” roadmap. You’ll cycle through it many times.

  flowchart TD
  A[Math & Python] --> B[Data Collection]
  B --> C[Data Cleaning & Preprocessing]
  C --> D[Exploratory Data Analysis]
  D --> E[Modeling]
  E --> F[Evaluation]
  F --> G[Iteration]
  G --> H[Deployment]
  H --> I[Monitoring]
  I --> G

Phase-by-phase (what you’ll build)

1. Foundations (vocabulary + intuition)
2. Preprocessing (where most real time goes)
3. Regression (predict numbers)
4. Classification (predict categories)
5. Ensembles (combine models)
6. Unsupervised (cluster/structure without labels)
7. Tuning (pipelines + CV + hyperparams)
8. Deep learning (neural nets)
9. NLP (text)
10. Deployment/MLOps (ship + monitor)

Two skill tracks to learn in parallel

Track A — Modeling skills

• pick baseline models
• avoid leakage
• choose metrics
• interpret results

Track B — Engineering skills

• write clean reproducible notebooks/scripts
• build pipelines
• version data and models
• deploy and monitor

Reality check: where time actually goes

Beginner expectation: “I’ll spend 80% training models.”

Real projects usually:

• 70% data cleaning and feature engineering
• 20% evaluation, iteration, and debugging
• 10% modeling

Mini-checkpoint

Write down:

• one ML problem you want to solve (ex: “predict house price”)
• your potential target label (ex: price)
• 5 candidate features (ex: bedrooms, area, age, location, condition)