Applied Machine Learning

Entry requirements

F01; F02 or a statistics diagnostic

What 36 hours means

Lessons

24 h

Capstone

8 h

Assessment

2 h

Remediation

2 h

Syllabus

Four modules, twelve lessons. Every lesson states what it covers and the evidence you produce for it.

• Module

  01

  3 lessons · 6 h

  

  Module 1

  Problem and data

  Problem framing · Exploratory analysis · Splitting and leakage

  M01Show lessonsHide lessons▾

  1. M01.01 · 120 min

     Problem framing

     Translate a maintenance problem into a target, prediction time and action.

     Evidence: A brief excludes information unavailable at prediction time.

  2. M01.02 · 120 min

     Exploratory analysis

     Inspect class balance, distributions and missing values.

     Evidence: An EDA notebook identifies at least three relevant data risks.

  3. M01.03 · 120 min

     Splitting and leakage

• Module

  02

  3 lessons · 6 h

  

  Module 2

  Models

  Preprocessing pipelines · Regression and classification baselines · Tree models

  M01Show lessonsHide lessons▾

  1. M01.04 · 120 min

     Preprocessing pipelines

     Fit imputers, encoders and scalers on training data only.

     Evidence:

• Module

  03

  3 lessons · 6 h

  

  Module 3

  Evaluation

  Imbalanced learning · Model selection · Thresholds and calibration

  M01Show lessonsHide lessons▾

  1. M01.07 · 120 min

     Imbalanced learning

     Compare class weights and training-only resampling.

     Evidence:

• Module

  04

  3 lessons · 6 h

  

  Module 4

  Delivery

  Interpretability and error slices · Model serving · Monitoring and handover

  M01Show lessonsHide lessons▾

  1. M01.10 · 120 min

     Interpretability and error slices

     Inspect feature influence and subgroup error patterns.

     Evidence:

Capstone

Build a predictive-maintenance classifier using an approved dataset. Compare a baseline and two models, prevent leakage, select a threshold and present operational tradeoffs.

Lab environment

CPU notebook

Lab infrastructure is not yet provisioned, so this describes the environment the course is designed for rather than one you can launch today.

Part of a pathway

• Applied AI Practitioner