Machine Learning Concepts for Analysts
Machine learning is reshaping analytics, and analysts need to understand its foundations. This course explains the main concepts and methods in accessible terms and shows where they fit within practical analysis work.
Course outline
What this programme covers, module by module.
- 01Explaining in plain terms what machine learning can and cannot do
- 02Distinguishing supervised from unsupervised learning methods
- 03Understanding how a model learns patterns from data
- 04Recognising why good training data matters so much
- 05Evaluating model performance sensibly and sceptically
- 06Identifying suitable machine learning use cases in the organisation
- 07Spotting overfitting and bias in a trained model
What you will gain
- Explain what machine learning can and cannot do
- Distinguish supervised from unsupervised methods
- Understand how models learn from data
- Recognise the importance of good training data
- Evaluate model performance sensibly
- Identify suitable use cases in the organisation
Who should attend
- Data and business analysts
- Reporting and MIS professionals
- Technical staff exploring analytics
- Anyone curious about machine learning
Upcoming sessions
| Date | City | Duration | Fees | Book |
|---|---|---|---|---|
| 27 to 29 Oct 2026 | London | 3 Days | US$1,500 | |
| 10 to 12 Nov 2026 | Dubai | 3 Days | US$1,500 | |
| 30 Nov to 2 Dec 2026 | Pretoria | 3 Days | R13,000 | |
| 11 to 13 Jan 2027 | Durban | 3 Days | R13,000 | |
| 15 to 17 Feb 2027 | Cape Town | 3 Days | R13,000 | |
| 26 to 28 Apr 2027 | Johannesburg | 3 Days | R13,000 | |
| 12 to 14 Jul 2027 | London | 3 Days | US$1,500 |
Fees are per delegate and exclude VAT. Public intakes are confirmed once minimum numbers are met. We also deliver this course on your own schedule, in-house or live online.
Enquire about this course
Register your team or ask for a tailored in-house quote. We reply within one working day.
WhatsApp usCourse brochure (PDF)- Fees from
- R13,000 per delegate
- Duration
- 3 Days
- Field
- Data & Analytics
- Formats
- In-house, public, online
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