Overview

Learn to Choose the Right ML Model is an intermediate course for data scientists, ML engineers, and analytics-minded developers who want to make model choices you can defend—not just experiment and hope for the best. As machine learning powers more business-critical systems, success depends on moving beyond intuition and automating robust, fair, and metrics-driven selection and deployment. In this course, you’ll practice structured problem typing, compare major algorithm families, and apply real-world metrics to pick and monitor models that work in the wild. You'll learn through case studies (like Zillow, Apple Card, and Google Flu Trends), hands-on labs with Python and scikit-learn, and scenario-driven coaching. By the end, you’ll be able to frame ML problems, select and justify models, automate fairness and drift checks, and deploy pipelines you can trust—so your solutions succeed, not just on paper, but in production.

Syllabus

Lesson 1: Classify ML Problems & Data Characteristics

In this opening lesson, learners see how correctly typing a machine-learning problem and inspecting data traits set the stage for every modeling decision. Guided by the Zillow Offers collapse (Problem: mis-priced homes from data drift; Why It Matters: $420 M loss), you'll practise spotting regression vs classification tasks, gauging feature quality, and flagging distribution shifts before they derail a project. Videos, a data-profiling lab, and a peer discussion build the analytical eye needed to choose the right model family with confidence.

Lesson 2: Compare Model Families & Suitability

In this lesson, learners will analyze the strengths and limitations of the most widely used machine learning model families—linear models, tree-based ensembles, clustering, and deep learning—to understand when and why each is best applied. The lesson focuses on why simply “trying every algorithm” leads to wasted effort, and how matching problem type and data structure to the right family enables smarter, faster, and more defensible results.Real-world failures, such as the Amazon recruiting engine bias, illustrate the pitfalls of poorly chosen models. Through scenario-based videos, guided readings, peer discussions, and hands-on labs, learners will practice comparing algorithms for fairness, performance, and interpretability—shifting from a toolbox mindset to strategic model selection.

Lesson 3: Evaluate & Select with Metrics-Driven Workflows

In this lesson, learners discover how wiring continuous evaluation into every training and deployment step transforms model delivery from a sprint of experiments into a reliable, data-driven decision engine. A midnight release scenario—where an unmonitored metric drifted and customer limits halved unexpectedly—shows why automated checks must begin with the very first cross-validation split and extend into live A/B tests.Learners investigate practical tooling—MLflow for experiment tracking, Optuna for automated hyper-parameter tuning, Evidently for production drift alerts, and GitHub Actions workflows for reproducible evaluation—to ensure issues surface before a model reaches end users. Case studies of metric blindness and data drift (e.g., Apple Card’s gender-bias probe and Google Flu Trends’ over-forecasting) demonstrate how small oversights in monitoring or retraining cadence can spiral into reputational or financial damage, reinforcing the need for continuous oversight.Hands-on demonstrations guide participants through:• setting quantitative success criteria that mix accuracy, fairness, and cost• configuring gates that fail a training run when key metrics regress• running a live A/B test and interpreting uplift with statistical rigor—all without slowing delivery velocity.By the end of the lesson, learners will know both how to embed metric-driven workflows into real pipelines and why treating evaluation as an afterthought is no longer acceptable—validation must be continuous, integrated, and owned by every stakeholder in the ML lifecycle.