Machine Learning Engineering Foundations

What you will be able to do

• Translate a business goal into a well-scoped ML problem: pick the task type, define the target variable, and recognize when a simple rule beats a model
• Set an honest baseline and choose success metrics tied to the real cost of false positives and false negatives before writing any training code
• Build train, validation, and test splits that avoid data leakage, including time-based splits for temporal data
• Engineer features with reproducible pipelines that encode categoricals, scale values, and impute missing data without contaminating the test set
• Train and compare model families (linear and logistic regression, decision trees, gradient boosting, and neural networks) and diagnose overfitting using bias-variance reasoning
• Tune models responsibly with k-fold cross-validation and grid, random, or Bayesian hyperparameter search, tracked as reproducible experiments
• Choose and defend the right metric for the job: confusion matrix, precision, recall, F1, ROC-AUC, and PR-AUC for classification, and RMSE, MAE, and R2 for regression
• Move a model from offline scores to online proof by setting thresholds, checking calibration, running an A/B test, and monitoring for data and concept drift

What is inside

4 modules, 8 lessons. Each module ends in a graded quiz and most carry an assignment.

1. 01

   Framing the ML Problem

   Most failed ML projects were lost at the framing stage, not the modeling stage. This module teaches you to turn a business goal into a precise learning task using the CRISP-DM lifecycle: choosing the task type, defining the target variable, and deciding honestly when a rule beats a model. You will set a baseline and pick success metrics tied to the cost of false positives versus false negatives, all before writing a line of training code.

   2 lessons · 5 quiz questions

2. 02

   Data Preparation and Feature Engineering

   Model quality is capped by data quality, and this is where ML engineers spend most of their time. This module shows you how to build train, validation, and test splits that prevent data leakage (including time-based splits for temporal data), then engineer features through reproducible transforms that encode categoricals, scale values, and impute missing data. You will work in scikit-learn style pipelines and learn where the feature store fits as the tool category that serves the same features in training and production.

   2 lessons · 5 quiz questions · assignment

3. 03

   Training and Tuning Models

   You have the data pipeline; now you train the model. This module tours the model families a machine learning engineer reaches for every day (linear and logistic regression, decision trees, gradient boosting, and neural networks) and shows how one shared training loop, powered by gradient descent, fits them all. Then it confronts the problem behind every failed model: overfitting. You will learn to read the bias-variance tradeoff correctly, control it with regularization and k-fold cross-validation, search hyperparameters with grid, random, and Bayesian methods, and keep every run reproducible with experiment tracking.

   2 lessons · 5 quiz questions

4. 04

   The Metrics That Tell You a Model Works

   A model is only as trustworthy as the metric you judged it by, and the wrong metric hides real failure. This module builds the classification metrics (confusion matrix, precision, recall, F1, ROC-AUC, and PR-AUC), the regression metrics (RMSE, MAE, and R2), threshold selection, and why plain accuracy lies on imbalanced data. It then moves from offline scores to online proof: checking calibration, designing an A/B test, and monitoring for data and concept drift once the model is live.

   2 lessons · 5 quiz questions · assignment