Machine Learning for Business, Step by Step for 2027

Lead machine learning projects without building models yourself: frame the right problem, judge whether a model is good enough and keep it fair and monitored, with no equations or code.

Who it's for

For managers, team leaders, consultants and founders who commission, sponsor or lead machine learning projects but do not build models themselves. It covers predictive machine learning built on your own organisation’s data and follows a project from first idea to a model that is live, monitored and fair.

What you'll learn

• Explain in plain words how a model learns from examples, and apply a three-question test to see whether a business problem suits machine learning.
• Match business questions to the six main problem types, such as classification, regression, clustering and recommendation, and describe the output you will get.
• Frame a prediction task before any data work – decision, target, prediction point, action, cost of each error and a baseline to beat – on a one-page canvas.
• Prepare data and features with the business in the room, avoid data leakage and split the data so test results can be trusted, all within UK GDPR.
• Read a confusion matrix, use precision and recall instead of accuracy, and set a threshold from the cost of errors and your team’s capacity.
• Run a project through the six CRISP-DM phases, time-box a proof of concept and decide whether to buy or build.
• Deploy and monitor models for drift, record each one in a model card (a format proposed by Margaret Mitchell and co-authors) and test for fairness and explainability.

What's inside

• 60 pages, 8 chapters
• Every chapter follows the same pattern: key concepts, a step-by-step method, an illustrative example from a fictional UK organisation, a ready-to-use tool, common mistakes, key steps and a “Try it” exercise
• Tools include an ML suitability test, problem-to-model map, ML problem framing canvas, data and feature checklist, model evaluation scorecard, project brief for data scientists, model monitoring plan with model card, and fairness and explainability checklist
• Questions to ask your data scientist before you say yes to a model
• A 30-60-90 day action plan with checklists, plus a glossary
• PDF in A4 format — read on screen or print
• Instant download after purchase

Contents

1. How Machine Learning Works
2. Matching Business Problems to Model Types
3. Framing the Problem Before Any Data Work
4. Preparing Data and Features
5. Judging Whether a Model Is Good Enough
6. Running an ML Project from Idea to Launch
7. Deploying and Monitoring Models
8. Making ML Fair, Explainable and Accountable

Part of The Business Library

This title is one of the 55 ebooks in The Business Library, available together for £109 (worth £1,437 if bought separately: the total of the individual prices of the included titles in our store). It is also included in The Ultimate Business Bundle, which adds 8 checklists and toolkits and 10 workbooks — all 73 products for £147.