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$20

Data Science for AI

Build the statistical foundation for AI: data quality, regression, classification, time series, neural networks, and when to use classical ML vs deep learning vs foundation models.

"For this churn problem I'd use XGBoost, not GPT-4 — here's the confusion matrix and business cost analysis proving why"

6 Modules

Each module builds on the previous one. By the end, you have a complete production system.

  1. 1

    Data Thinking

    EDA skills: distributions, quality issues, correlation vs causation

  2. 2

    Regression & Prediction

    Linear/logistic regression, features, overfitting, evaluation metrics

  3. 3

    Classification & Clustering

    Decision trees, random forests, K-means, precision/recall trade-offs

  4. 4

    Time Series & Forecasting

    Trend decomposition, ARIMA, anomaly detection, chronological validation

  5. 5

    Neural Networks Demystified

    Layers, backpropagation, CNNs, transformers, when NNs are overkill

  6. 6

    From Statistics to Foundation Models

    Paradigm selection framework: classical ML vs DL vs foundation models

Production patterns you'll master

Exploratory Data AnalysisTrain/Test SplitsFeature EngineeringConfusion MatricesTime Series DecompositionParadigm Selection

Synthetic data included

  • Housing dataset (JSON)
  • Churn data with labels
  • Monthly sales (CSV)
  • Neural network training logs
  • Model cost comparisons
  • Paradigm decision trees

What you walk away with

Shareable portfolio

A public URL showing your module timeline, patterns mastered, and completion status.

All the code

Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.

Module walkthrough

Each module documented with deliverables and the production pattern you implemented.

Ready to build your data science for ai?

First course free. $20 per course after that.