Applied Machine Learning (PDMV Framework)
Machine learning for professionals who need results, not just models. Learn to frame problems, plan data strategy, and measure real business impact.
The framework
Problem-Data-Model-Value (PDMV)
PDMV is a decision framework for applied ML: (P) Define the business problem and success metric before touching data; (D) Understand data quality, bias, and what it can and cannot tell you; (M) Choose the simplest model that solves the problem; (V) Measure business value, not just accuracy.
How you'll learn
Text-first. Practice-heavy. No video lectures.
15-minute text lessons
Short, dense reading you can finish between meetings — not hour-long video lectures.
Hands-on exercises
Apply the framework to a realistic scenario, right in the module where you learned it.
Quizzes & assessments
Check your judgment, not just your memory — most questions are situational.
Certificate on completion
Finish a course and get a shareable certificate, automatically.
About this course
The biggest mistake in ML is starting with a model and looking for a problem. PDMV reverses this: you start with the business Problem, understand your Data, choose the right Model, and measure real-world Value. Updated for 2026: includes AutoML, foundation models for tabular data, responsible AI under the EU AI Act, and modern experiment tracking.
The project: Customer Churn Prediction System
Work with a realistic, messy telecom dataset to build a churn prediction model. You will clean data, engineer features, compare models using the PDMV framework, and present findings in business terms to a simulated executive audience.
- 1Free preview
The PDMV Framework Introduction
Why starting with models is the biggest ML mistake.
30 min· Knowledge check - 2
Problem Definition — Before You Touch Data
Define the business problem, success metric, and constraints.
30 min· Knowledge check· Coding exercise - 3
Data Understanding & Quality Assessment
Explore, profile, and assess your dataset for ML readiness.
35 min· Knowledge check· Coding exercise - 4
Feature Engineering — Creating Signal from Noise
Transform raw data into features that models can learn from.
35 min· Knowledge check· Coding exercise - 5
Model Selection — The Simplest Model That Works
Choose models based on problem type, data size, and interpretability needs.
30 min· Knowledge check· Coding exercise - 6
Value Measurement — Beyond Accuracy
Translate model performance into business value.
25 min· Knowledge check· Coding exercise - 7
Responsible AI & Model Governance ★ NEW
45 min· Knowledge check - 8
Capstone: Customer Churn Prediction
Build an end-to-end churn prediction system using PDMV.
35 min· Knowledge check· Coding exercise