Python, Data & Mathematics Foundations
₦150,000Basic computer literacy; see course entry requirements.
Choose your stage below ↓PRACTICAL LEARNING / ITC COMPUTERS
AI / Machine Learning
Explore how data-driven models solve practical problems. Programming foundations are recommended.
₦350,000 · group full-payment tuitionSix months · 24 teaching weeks
Choose a package & enquire →Who can join?
Basic computer literacy, basic algebra and willingness to code. Python preparation or a readiness assessment is recommended.
How you learn
Online and Offline (in-person) training available. Instructor-led lessons, demonstrations and practical exercises. Schedule and intake are confirmed before enrolment. ITC confirms practical labs, equipment and attendance arrangements for your chosen mode.
Assessment
Practical exercises, assignments and a final project or skills assessment.
One stage or the full pathway.
Join a stage that matches your skills. Later-stage entry requires previous-stage completion or an assessment.
Machine-Learning Models & Evaluation
₦150,000Successful completion of the preceding stage, or an entry assessment demonstrating equivalent skills.
Choose your stage below ↓Applied AI & Deployment
₦150,000Successful completion of the preceding stage, or an entry assessment demonstrating equivalent skills.
Choose your stage below ↓What you’ll learn and practise
Six months comprises 24 teaching weeks; holiday dates are confirmed with each intake. A typical six-month timetable is three two-hour sessions weekly plus independent practice; confirm your cohort’s teaching hours.
WEEKS 1–2 AI & ML Foundations
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Use cases, learning methods, responsibility and limitations
Practical: Define a suitable prediction problem.
WEEKS 3–4 Python Foundations
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Variables, functions, loops, exceptions and notebooks
Practical: Write and debug small programs.
WEEKS 5–6 Data Preparation
Available online or offline (in-person), with practical arrangements confirmed by ITC.
NumPy, pandas, missing values and exploration
Practical: Prepare a dataset.
WEEKS 7–8 Mathematics & Statistics
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Probability, variation, vectors and relationships
Practical: Explore concepts through visual examples and code.
WEEKS 9–10 Regression
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Features, targets, linear regression and errors
Practical: Build a numerical prediction model.
WEEKS 11–12 Classification
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Classifiers, decision trees and class imbalance
Practical: Build and compare classifiers.
WEEKS 13–14 Model Evaluation
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Training/validation/test splits, overfitting and leakage
Practical: Evaluate models with appropriate metrics.
WEEKS 15–16 Unsupervised Learning
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Clustering and dimensionality reduction
Practical: Explore patterns in unlabeled data.
WEEKS 17–18 Neural-Network Foundations
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Layers, training and introductory deep learning
Practical: Train a small neural network.
WEEKS 19–20 Generative AI & AWS AI Concepts
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Prompting, APIs, retrieval, AWS service use cases and privacy
Practical: Prototype a limited-scope assistant.
WEEKS 21–22 Deployment & Responsible AI
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Interfaces, monitoring, fairness, security and governance
Practical: Present a model interface and limitations.
WEEKS 23–24 Capstone & Assessment
Available online or offline (in-person), with practical arrangements confirmed by ITC.
Applied modelling and certification readiness
Practical: Build, evaluate and explain an AI project.
What you gain
- Explore how data-driven models solve practical problems. Programming foundations are recommended.
- Demonstrate practical work, document decisions and explain limitations.
- Complete a final project or skills assessment and build evidence of learning.
International certification pathway
Certification preparation is integrated at the relevant stages. A checkpoint means reviewing the awarding body’s full objectives, completing supplementary preparation and passing a readiness assessment; attendance alone does not establish exam readiness.
PCEP — Entry-Level Python Programmer
Paid external examination
Cover the full Python syllabus and demonstrate independent coding readiness. This validates Python, not ML engineering.
Official qualification information ↗AWS Certified AI Practitioner
Paid external examination
Add AWS-specific AI services, use cases, responsible AI and governance. This is a foundational qualification.
Official qualification information ↗Certificates and examination fees
Successful completion of ITC training and assessment earns an ITC Computers Certificate of Completion. This is separate from any external qualification. International examination, registration and certification charges are not included in tuition and are paid by the learner. ITC helps identify suitable free opportunities and promotions where available; availability is verified before booking. Free completion certificates and badges are distinguished from professional certifications. No external pass or employment is guaranteed.
Tools, materials and support
Optional software subscriptions, internet/data and external examination fees are separate from tuition.
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