Full-time Hybrid
Online + in-person classes | Mon–Fri | 8am–5pm EAT
Physical classes: Applewood Adams, 13th Floor, Ngong Road, Nairobi.
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Work thoughtfully with AI
Applied AI
A grounded introduction to applied AI that helps learners move beyond the hype and use intelligent tools with clarity, care, and practical intent.
Duration
12 weeks
Next intake
October 1st, 2026
Learning format
Experiments, guided studio work, and feedback
The LEA learning experience
A practical pathway for real progress.
A grounded introduction to applied AI that helps learners move beyond the hype and use intelligent tools with clarity, care, and practical intent. Leave with a small library of useful workflows, stronger judgement, and a practical point of view on responsible AI use.
Python + data literacy
Reliable AI workflows
Evaluation & safety
Flexible ways to learn
Choose your learning rhythm.
Pick the rhythm that gives you the clearest space to learn, practise, and keep moving.
What you will build
Leave with work people can see and use.
Move your cursor over a poster to see the build, tools, and portfolio evidence.
Project 01
DATA LAB
Python data brief
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DATA LAB
Python data brief
A reproducible notebook that cleans a small local dataset, explains patterns, visualises uncertainty, and records data provenance.
PythonCSV/JSONNotebook and charts
Evidence: Notebook, cleaned data, data dictionary, visualisations, assumptions, and a two-minute explanation.
Project 02
AI WORKFLOW
Customer-support AI assistant
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AI WORKFLOW
Customer-support AI assistant
A support workflow that classifies questions, drafts replies, cites approved information, and sends uncertain cases to a human.
LLM API or approved local modelStructured JSON outputKnowledge base
Evidence: Fixed evaluation set, accuracy and failure table, prompt versions, escalation rules, and privacy checklist.
Project 03
RESEARCH
AI research and verification studio
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RESEARCH
AI research and verification studio
A source-backed research brief produced with AI assistance but verified by the learner against primary sources and recorded corrections.
Search workflowCitationsPython extraction helpers
Evidence: Research log, evidence table, corrections log, uncertainty notes, and final brief.
Project 04
VISION & PREDICTION
Vision or prediction prototype
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VISION & PREDICTION
Vision or prediction prototype
A small, clearly scoped computer-vision or forecasting prototype such as object counting, barcode scanning, weather trend analysis, or transport-demand exploration.
PythonCamera or tabular dataEvaluation and consent
Evidence: Dataset note, prototype, test cases, limitations, privacy review, and a recommendation for the next iteration.
API work starts with mocks and sandboxes. Daraja projects never handle real money in class. Google device-management examples use mock devices or read-only test data; learners do not lock, wipe, or change real devices.
Inside the pathway
Curriculum breakdown.
6 modules · 1 break included
Weeks 1–2
AI & data foundations
Weeks 1–2
AI & data foundations
Build a useful mental model of modern AI systems, data quality, Python, and the limits that shape responsible use.
Build project
Create a Python notebook that explores a small dataset and an AI use-case map with a go, no-go, or pilot recommendation.
Covers:AI vocabularyPython basicsData qualityUse-case mapping
Weeks 3–5
Python data lab
Weeks 3–5
Python data lab
Use Python to clean, inspect, visualise, and explain data before deciding whether AI belongs in the workflow.
Build project
Produce a reproducible data brief with provenance, assumptions, and a clear explanation of what the data cannot prove.
Covers:CSV & JSONData cleaningVisualisationUncertainty
Week 6
Studio reset
Week 6
Studio reset
Step back from the tools, review what is working, and get feedback before moving into responsible practice.
Build project
Audit the workflow, remove unnecessary automation, and document the revised human review step.
Covers:Rest & reflectionWorkflow reviewMentor check-in
Weeks 6–7
Prompt & workflow design
Weeks 6–7
Prompt & workflow design
Turn repeated tasks into clearer, more reliable workflows with structured outputs, versioned prompts, and human approval.
Build project
Design an admissions or support workflow assistant and evaluate it on a fixed test set.
Covers:Prompt structureSchemasTool boundariesQuality checks
Week 8
Research & verification
Week 8
Research & verification
Use AI to accelerate exploration without outsourcing judgement, accuracy, or the responsibility to verify.
Build project
Produce a source-backed research brief with an evidence table and corrections log.
Covers:Source checkingFact verificationCitationsUncertainty
Weeks 9–10
Responsible AI practice
Weeks 9–10
Responsible AI practice
Explore privacy, bias, safety, prompt injection, and the judgement needed to use intelligent tools with care.
Build project
Review a fictional learner-support workflow for privacy, bias, unsafe decisions, and escalation requirements.
Covers:PrivacyBias & fairnessSecurityHuman oversight
Weeks 11–12
Applied AI studio
Weeks 11–12
Applied AI studio
Design, test, and present a small AI-supported project connected to a meaningful real-world outcome.
Build project
Build a human-in-the-loop learner-support assistant with a Python service, approved information, citations, and escalation.
Covers:Python serviceKnowledge groundingEvaluationShowcase