Unlocks after Module 3The AI Engineering Program

Module 4: Data, SQL & Machine Learning in Practice

Query real business data with SQL, build and honestly evaluate classical ML models, and explain every metric to a manager.

26guided hours

12lessons

4 of 13in the program

Lessons

12 lessons, in order. Each one unlocks when you finish the one before it.

4.1
SQL for analysis: SELECT, filters, joins on a real sales database
4.2
Aggregations, window functions and CTEs
4.3
SQL from Python, and loading query results into pandas
4.4
The scikit-learn workflow: splits, pipelines, and leakage-proof preprocessing
4.5
Linear and logistic regression in practice
4.6
Decision trees, random forests and gradient boosting
4.7
Metrics that match the business: precision, recall, ROC-AUC, cost-based thresholds
4.8
Cross-validation and hyperparameter tuning
4.9
Feature engineering for tabular data and text (TF-IDF)
4.10
Clustering and customer segmentation
4.11
Model cards: reporting what a model can and cannot do
4.12
Module project

AI vs ML vs DL vs GenAI: deep notes, a hands-on lab and a quiz. No card. No catch.