How is this course delivered?

This is a tutor-led course delivered live online, taught in English (Uganda). Your tutor guides you through the curriculum live rather than leaving you to self-study pre-recorded videos.

Will I get a certificate?

Yes. You'll receive a mathematics for ai certificate of completion after completing the course requirements.

What do I need to know before I start?

You should have: Successful completion of Course:Data Literacy & Spreadsheets for Analysis; Basic arithmetic and secondary-school mathematics; Basic computer skills; Ability to follow simple formulas and graphs.

How long does the course take, and what does it cost?

The course runs for 8 weeks and costs UGX 600,000. Paying for one programming course also unlocks 25% off your next one.

What do I need to have ready to join?

You'll need: Computer; Reliable internet connection; Microsoft Excel or compatible spreadsheet software; Python 3; NumPy; Jupyter Notebook or Google Colab; Basic calculator; Notebook and pen.

Can I get help if I get stuck?

Yes. Enrolled students can book one-on-one tutor sessions any time through Get Help, in addition to regular class time.

Programming Upper Secondary · University · General PublicMathematics for AI (Statistics & Algebra Refresher)

Machine Learning

Upper Secondary · University · General Public

Mathematics for AI (Statistics & Algebra Refresher)

Duration

8 weeks

Investment

UGX 600,000

Certificate

Included

Teaching

Live online

Enroll in this tutor-led course Get Help

Program Introduction

Build the practical mathematical foundation needed to understand data science, machine learning and artificial intelligence. Designed for learners in Uganda, East Africa and across Africa, this beginner–intermediate course refreshes algebra, probability, descriptive statistics, vectors, matrices and basic calculus through plain-language explanations, visual examples, Excel exercises and NumPy practice. Local examples include rainfall, crop yield, boda-boda fuel costs, mobile money and small-business data.

Key Features & Benefits

• Plain-language mathematics for AI • Visual explanations of functions, vectors and matrices • Uganda and East Africa case examples • Hands-on Excel and NumPy exercises • Practice with rainfall, crop-yield and transport-cost data • Correlation-versus-causation reasoning • Applied capstone mini-project • Foundation for Course 7

Real-World Applications

• Prepare for machine learning, data science and AI study • Analyse rainfall and crop-yield patterns • Summarise business, survey and monitoring data • Interpret mobile-money, sales and transport-cost datasets • Check whether variables are correlated without assuming causation • Create simple trend-based forecasts in Excel • Support evidence-based decisions in agriculture, NGOs, research and small businesses

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

Mathematics for AIAI Mathematics UgandaStatistics for AIAlgebra RefresherProbabilityLinear AlgebraDescriptive StatisticsNumPyExcel Data AnalysisMachine Learning FoundationsData Science UgandaAI Training UgandaEast Africa AI CoursesAfrica Digital Skills

Teaching format

Live online

Teaching language

English (Uganda)

Intended learners

Upper Secondary · University · General Public

What you will learn

Interpret functions, graphs and simple rates of change
Calculate and explain mean, median, mode and standard deviation
Solve basic probability questions and interpret common probability distributions
Represent and manipulate vectors and matrices by hand and with NumPy
Distinguish correlation from causation in real datasets
Use Excel to organise data, calculate statistics and draw a simple trend line
Explain how algebra, statistics, probability and calculus support AI models
Complete a mini-project forecasting a boda-boda fuel cost from historical data

Modules

1
Algebra refresher
Review the algebra used in data analysis and AI
Variables and expressionsEquationsFunctionsCoordinate graphsReading slopes and intercepts
2
Probability basics
Understand uncertainty through familiar East African examples
Events and outcomesFractions, decimals and percentagesSimple probability rulesOdds of rain during a planting season
3
Descriptive statistics
Summarise and compare datasets accurately
MeanMedianModeRangeVarianceStandard deviationInterpreting outliers
4
Vectors and matrices, visually
Build intuition for the structures used in NumPy and machine learning
VectorsVector operationsMatricesRows and columnsMatrix shapesBasic matrix operations in NumPy
5
Correlation versus causation
Interpret relationships between variables without overstating conclusions
Scatter plotsPositive and negative correlationConfounding factorsRainfall and crop-yield example
6
Basic calculus intuition
Understand rate of change without advanced proofs
Change over timeSlope as rate of changeGradients in plain languageWhy optimisation matters in AI
7
Probability distributions in plain language
Recognise how values and uncertainty may be distributed
Discrete and continuous dataNormal distribution intuitionExpected valueVariation and spreadReading distribution graphs
8
Math Behind AI capstone
Apply the course concepts to a practical Uganda-based mini-project
Collect or use sample boda-boda fuel-cost dataCalculate descriptive statisticsPlot costs in ExcelFit a simple trend linePredict the next month's costExplain assumptions and limitations