Building & Training ML Models

What you will be able to do

• Frame a problem as supervised or unsupervised, diagnose it through the bias-variance tradeoff, and pick the model family whose assumptions fit the data and the constraints
• Train and interpret the core supervised families: linear and logistic models fit with gradient descent, and tree ensembles including random forests and gradient boosting (XGBoost, LightGBM)
• Choose loss functions and optimization settings deliberately, and read learning curves to tell an underfit model apart from an overfit one
• Apply unsupervised methods to unlabeled data: cluster with k-Means, hierarchical, and DBSCAN, and reduce dimensionality with PCA, t-SNE, and UMAP
• Control overfitting with L1, L2, and elastic-net regularization, early stopping, dropout, and tree pruning, and explain what each does to model complexity
• Run hyperparameter search (grid, random, and Bayesian optimization) inside a proper cross-validation scheme so tuning does not leak information or inflate your scores
• Design a rigorous validation protocol: choose the right cross-validation strategy, prevent data leakage, use nested cross-validation when tuning, and report honest, well-chosen metrics

What is inside

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

1. 01

   Framing the Problem and Choosing a Model Family

   Before you train anything, you decide what kind of problem you have and which family of model can solve it. This module separates supervised from unsupervised learning, grounds the course in the bias-variance tradeoff, and tours the main model families (linear models, trees, kernel methods, nearest neighbors, and neural networks) with the assumptions and costs of each. You leave able to match a problem to a short list of candidate models instead of defaulting to whatever you reached for last.

   2 lessons · 5 quiz questions

2. 02

   Building & Training ML Models

   Module 2 of the Advanced course, targeting the Machine Learning Engineer role. Two reading-first lessons take learners from linear and logistic models (loss functions and gradient descent) through tree ensembles (random forests and gradient boosting with XGBoost and LightGBM), plus a 5-question quiz and a hands-on loan-default modeling assignment.

   2 lessons · 5 quiz questions · assignment

3. 03

   Unsupervised Learning: Finding Structure Without Labels

   When there are no labels, you still have to find structure, compress features, and understand what your data is made of. This module builds working fluency in the two pillars of unsupervised learning. You will cluster with k-Means, hierarchical linkage, and density-based DBSCAN, and you will reduce dimensions with PCA for linear structure and t-SNE and UMAP for visualization. Just as important, you will learn the traps that make unsupervised results easy to over-read, so you can judge honestly whether the structure you found is real.

   2 lessons · 5 quiz questions

4. 04

   Regularization, Tuning, and Rigorous Validation

   This module is where a model that demos well becomes a model you can trust in production. You will learn to diagnose overfitting through the bias-variance lens and fight it with L1, L2, early stopping, and dropout, then search the hyperparameter space efficiently with random and Bayesian methods, and wrap everything in a validation protocol strict enough to trust. The organizing question throughout: can you prove your model will generalize, or did you just fit the test set by accident?

   2 lessons · 5 quiz questions · assignment