Applied Machine Learning

About this course

Build machine learning projects around deployment conditions, business decisions and honest evaluation rather than a single notebook score.

This practitioner course is part of the AI & Generative AI school. Work through the curriculum below in a self-paced / instructor-led format, with an estimated study time of 20 h.

Course curriculum

• Module 1 — Framing Applied Machine Learning Problems
• Module 2 — Validation That Matches Deployment
• Module 3 — Feature Engineering for Tabular Data
• Module 4 — Gradient Boosting in Practice
• Module 5 — Probabilities, Costs and Decisions
• Module 6 — Interpretation and Error Analysis
• Module 7 — Forecasting with Machine Learning
• Module 8 — From Notebook to Reliable Project

Applied learning

• Case study — Harbourline's late-delivery model: from 0.89 to a model that works
• Lab 1 — Leak hunt and honest validation
• Lab 2 — From model to decisions
• Lab 3 — Depot volume forecast
• Capstone — An Applied ML Decision System

Who this course is for

Technology Professional; Data Professional; Engineer / Architect

Prerequisites

NE-AI-106

Course details

• Course code: NE-AI-201
• Level: Practitioner
• Estimated duration: 20 h
• Delivery: Self-Paced / Instructor-Led