AI & Data ScienceNational Certificate Level IV100% Scholarship Eligible
Applied Artificial Intelligence & Machine Learning
Deep Learning, PyTorch, LLM Engineering, RAG & Agentic Systems
Transition from foundational mathematics and computer vision to building cutting-edge production AI applications. Train deep neural networks with PyTorch, fine-tune open-weight LLMs, implement Retrieval-Augmented Generation (RAG) with vector databases, and deploy autonomous agentic workflows on high-performance compute infrastructure.
Duration
9 months
Proficiency
Advanced
Start Date
November 2026
Credit Units
90 Units
Accredited TVETCertification:National Certificate in Applied AI & Machine Learning
Tuition Fee:₦350,000 (100% Scholarship Eligible)
Study Mode:Hybrid (Lab + Online)
Accreditation:National Skills Qualification Framework (NSQF)
Terrain-Optimized Learning
Syllabus & Real Engineering Modules
Designed for Nigerian connectivity and power constraints. Study online or export complete lesson guides for offline learning.
Key Topics & Theoretical Concepts
Linear Algebra: Matrices, Dot Products, Vector Spaces & SVD
Multivariate Calculus: Gradients, Jacobians, Hessians & Chain Rule
Probability Distributions, Maximum Likelihood Estimation & Bayes' Theorem
High-Performance Vectorized Computations in NumPy & SciPy
Exploratory Data Analysis, Cleaning & Feature Normalization
Hands-On Lab: Build a Custom Linear Algebra & Gradient Descent Engine from ScratchIntermediate
Implement matrix multiplication, automatic differentiation, and stochastic gradient descent without using PyTorch or TensorFlow.
Toolchain & Stack:
Python 3.12NumPyMatplotlibPyTest
Recommended Module Literature
Mathematics for Machine Learning
Source: Marc Peter Deisenroth (Cambridge University Press) • The mathematical foundations of regression, PCA, and neural networks.
Graduation Competencies
✓
Train, fine-tune, and evaluate deep learning and transformer models from scratch✓
Architect production RAG systems with hybrid vector retrieval and semantic re-ranking✓
Deploy scalable, low-latency LLM inference clusters using vLLM and Docker✓
Implement robust AI safety guardrails and automated MLOps monitoring pipelinesTechnologies Mastered
Python 3.12PyTorchHugging FaceLangChain / LlamaIndexpgvector / PineconeLoRA / QLoRA Fine-TuningvLLM / OllamaComputer Vision (YOLO)
Prerequisites
- •Proficiency in Python programming & linear algebra basics
- •Understanding of probability, calculus, and matrix manipulation
- •Familiarity with git and virtual environments
Target Industry Roles
AI Systems EngineerMachine Learning EngineerApplied LLM SpecialistPrompt & RAG ArchitectData Scientist