Cohort 20 — May/June 2026 · Admission Open
World-Class Courses in
AI, Robotics & Automation.
RAIN's curriculum is 70% product development. You are not here for lectures — you are here to build things that solve real problems and enter the market. Every course ends with a portfolio of sellable, deployable products. Founded by Dr Olusola Ayoola.
⭐ Flagship Programme
AIML
Artificial Intelligence & Machine Learning
The most comprehensive AI and Machine Learning training programme in Nigeria.
From Python fundamentals to Large Language Models, neural networks, and
deploying production-grade AI systems — all culminating in a real-world
capstone project built for African and global markets.
Complete Curriculum
Weeks 1–12
▾
Foundation — Python, Data Science & AI Fundamentals
12 weeks · Building the technical foundation
01
Python Programming — Foundations to Advanced
Variables, control flow, functions, OOP, file handling, APIs, and production-grade Python patterns. No prior programming experience required.
02
Data Science and Advanced Analytics
NumPy, pandas, data wrangling, exploratory data analysis, statistical inference, and visualisation with Matplotlib, Seaborn, Tableau, and Power BI.
03
Intro to AI and No-Code AI Tools
The history and landscape of AI, prompt engineering, no-code AI platforms, and how to prototype with AI before writing a single model.
04
Machine Learning I — Supervised Learning
Linear and logistic regression, decision trees, random forests, SVMs, k-nearest neighbours, cross-validation, and real-world ML pipelines with scikit-learn.
05
Machine Learning II — Unsupervised and Ensemble Methods
Clustering (k-means, DBSCAN), dimensionality reduction (PCA, t-SNE), gradient boosting, XGBoost, and ensemble stacking strategies.
06
Web and Desktop Application Development
Flask web development, RESTful APIs, SQLite / PostgreSQL integration, and Python-based GUI applications for AI model deployment.
07
Git, GitHub and Cloud Services
Version control with Git, collaborative workflows, CI/CD basics, AWS and Azure fundamentals, and deploying models to the cloud.
08
Time Series Forecasting
ARIMA, SARIMA, Prophet, LSTM-based forecasting — applied to financial, health, and agricultural datasets.
09
Database Management for AI
SQL, database design, data pipelines, and data warehousing — building the data infrastructure that AI systems run on.
10
Cybersecurity Fundamentals for AI Engineers
Threat modelling, secure coding practices, and protecting AI pipelines and deployed APIs.
11
Project Management for AI Products
Agile, Scrum, PRINCE2 concepts — how to manage AI projects from ideation to delivery.
12
Mini-Project — Semester 1 Deliverable
A working end-to-end ML pipeline solving a real problem — from data collection to deployed model.
Weeks 13–34
▾
Advanced — Deep Learning, Computer Vision & NLP
22 weeks · Advanced AI systems and specialisation
01
Deep Learning and Neural Network Architectures
Perceptrons, multi-layer networks, backpropagation, activation functions, batch normalisation, dropout — built from scratch with TensorFlow and PyTorch.
02
Convolutional Neural Networks (CNNs)
Image classification, object detection (YOLO, SSD), image segmentation — applied to medical imaging, agriculture, and industrial inspection.
03
Recurrent Neural Networks and LSTMs
Sequence modelling, text generation, sentiment analysis, and time-series prediction using RNNs and LSTMs.
04
Computer Vision and Image Processing
OpenCV pipelines, feature extraction, face recognition, optical flow, and real-time video processing.
05
Generative AI and GANs
Generative Adversarial Networks — image synthesis, data augmentation, deepfake detection, and creative AI applications.
06
Natural Language Processing (NLP)
Text preprocessing, word embeddings (Word2Vec, GloVe), named entity recognition, question answering, and sentiment analysis.
07
Large Language Models and Prompt Engineering
Fine-tuning LLMs, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and building LLM-powered applications.
08
Reinforcement Learning
Markov Decision Processes, Q-learning, policy gradients, and RL for decision-making systems.
09
AI Deployment, MLOps and Production Systems
Docker, Kubernetes, model monitoring, MLflow, model versioning, and building scalable AI systems that serve millions of users.
10
AI Ethics, Fairness and Responsible AI
Bias detection, algorithmic fairness, explainability (SHAP, LIME), and the regulatory landscape for AI in Africa and globally.
11
Specialisation Project — Advanced Deliverable
A working advanced AI system — CV pipeline, NLP system, or LLM application — submitted and reviewed by the faculty.
Weeks 35–52
▾
Capstone — Build, Deploy, Launch
18 weeks · Real product. Real problem. Real world.
01
Project Ideation and Scoping
Students identify a real-world African challenge — in healthcare, agriculture, education, security, or finance — and define a project that their AI solution will address.
02
Data Collection and Pipeline Engineering
Building the data infrastructure for the capstone — APIs, web scraping, sensor data, labelling, and creating production-quality datasets.
03
Model Development and Iteration
Full AI system development under mentor supervision — with weekly review sessions and peer critique.
04
Deployment and Product Launch
Deploying the product to cloud infrastructure, building a user interface, and preparing for public demonstration.
05
Research Documentation and Publication Prep
Writing the technical report and preparing material suitable for journal submission — feeding directly into the B.Sc. degree pathway.
06
Portfolio, GitHub and Professional Profile
Building a public GitHub portfolio, LinkedIn presence, and project documentation that international employers and graduate schools will see.
07
Performance Evaluation, Certification and Awards
Final assessment by RAIN faculty and external reviewers. Certificates issued. Outstanding projects recognised.
Core Technologies
Python
TensorFlow
PyTorch
scikit-learn
pandas
NumPy
OpenCV
Hugging Face
LangChain
AWS / Azure
Docker
Flask
SQL
Git
Tableau
Power BI
Ready to start AIML?
Next cohort: May/June 2026. Spaces are limited.
⭐ Flagship Programme
RDA
Robot Development & Automation
The most hands-on robotics and automation programme in Africa.
You will build real robots, programme real microcontrollers, deploy
real IoT systems, and develop autonomous vehicles — all using the same
hardware and software stack used by robotics engineers in global industry.
Complete Curriculum
Semester 1
▾
Hardware, Programming & IoT Foundations
Months 1–4 · Hands-on from Day 1
01
Intro to AI & Robotics + No-Code AI & Prompt Engineering
The landscape of robotics and AI — from industrial robots to humanoids. No-code AI tools and prompt engineering for immediate practical application before writing hardware code.
02
Python Fundamentals for Robotics
Python programming with a robotics focus — scripting, automation, data handling, and interfacing with hardware. Students write their first hardware control scripts in Week 2.
03
Product Design and Development
Design thinking, CAD fundamentals, product lifecycle, user research, prototyping methodology, and bringing ideas from concept to physical product.
04
Microcontroller Fundamentals — Arduino
Architecture of microcontrollers, GPIO, ADC/DAC, PWM, SPI, I2C, UART — programming Arduino for real physical outputs: LEDs, motors, sensors, displays.
05
Python for Database Management, Web Development & Excel
SQLite, PostgreSQL, Flask APIs, Excel automation — building the data layer that connects physical systems to software applications.
06
Internet of Things (IoT)
Home automation, industrial IoT, sensor networks, MQTT protocol, cloud connectivity — students build and deploy a working smart system.
07
Practical Electronics
Circuit theory, Ohm's Law, transistors, op-amps, motor drivers, PCB reading, and soldering. Students build and debug circuits on breadboard and PCB.
08
Advanced Microcontroller Fundamentals
Interrupts, timers, real-time operating systems (RTOS), multi-sensor fusion, and complex embedded system architectures.
09
Robot Manufacturing and Robotics-Aided Manufacturing
Mechanical design, 3D printing, CNC machining, robot assembly, actuator selection, and manufacturing automation concepts.
10
Python for Desktop GUI Applications
Tkinter and PyQt — building desktop interfaces that control robots and visualise sensor data in real time.
11
Git & GitHub, Cloud Services, Cybersecurity & Python for Chatbots
Professional software tools, cloud deployment, API security, and building natural language interfaces for robotic systems.
Semester 2
▾
Autonomous Systems, AI & Advanced Robotics
Months 5–8 · Building intelligent machines
01
Raspberry Pi, ROS and Controller Coordination
Linux on Raspberry Pi, Robot Operating System (ROS) architecture, nodes, topics, services — the backbone of modern robotics software.
02
Techniques for Digital Twinning
Creating virtual replicas of physical systems — simulating robot behaviour before deploying to hardware. Gazebo, RViz, and URDF robot models.
03
Control Systems Fundamentals and PID Control
Feedback loops, stability analysis, Laplace transforms, transfer functions — and PID tuning for precise motor and actuator control.
04
Theories in Neural Networks
Fundamentals of deep learning from a robotics perspective — how neural networks enable robots to learn from their environment.
05
Techniques for Computer Vision
OpenCV, real-time object detection (YOLO), face recognition, colour tracking, depth sensing with stereo cameras — robots that can see.
06
SLAM — Simultaneous Localisation and Mapping
How autonomous robots understand their environment. Kalman filters, particle filters, LiDAR-based mapping, and occupancy grid maps.
07
Path Planning Algorithms
A*, Dijkstra, RRT, potential fields — algorithms that tell robots how to navigate from A to B while avoiding obstacles.
08
GAN and Computer Vision Algorithms
Generative Adversarial Networks for synthetic training data, and advanced CV pipelines for robotics perception.
09
Techniques for Natural Language Processing
Building robots that understand human commands — NLP pipelines, speech recognition, and voice-controlled robotic interfaces.
10
Drone System Development
Quadcopter physics, flight controllers, PID for drones, GPS integration, autonomous mission planning, and safety protocols.
11
Robot Arm Kinematics
Forward and inverse kinematics, Denavit-Hartenberg parameters, workspace analysis, and programming 6-DOF robot arms.
12
Machine Learning and Intelligent Control
Applying ML to robotics — reinforcement learning for robot locomotion, behavioural cloning, and adaptive control systems.
Semester 3
▾
Capstone Project & Portfolio
Months 9–12 · Build something real. Can be done virtually.
01
Project Scoping and System Architecture
Students identify an industry or societal problem — healthcare, agriculture, security, manufacturing — and define a robotic system that addresses it.
02
Hardware Design and Prototyping
Building the physical prototype — PCB design, 3D printing, motor selection, sensor arrays, and power management.
03
Software Architecture and Integration
ROS integration, sensor fusion, control algorithms, computer vision pipelines — bringing all semester learning into one coherent system.
04
Testing, Iteration and Performance Validation
Systematic testing protocols, performance benchmarking, fault diagnosis, and iteration cycles under faculty supervision.
05
Project Documentation and Research Writing
Technical reports, system documentation, and research-quality writing that feeds into the degree pathway publication pipeline.
06
Portfolio, GitHub, LinkedIn and Professional Branding
A public-facing portfolio of completed projects, code repositories, and professional online presence ready for employers.
07
Demonstration, Certification and Awards
Final public demonstration of the capstone. Certificates issued. Outstanding projects recognised and rewarded.
Core Technologies
Python
C++
Arduino
Raspberry Pi
ROS
SLAM
PID Control
OpenCV
TensorFlow
IoT / MQTT
Gazebo / RViz
3D Printing / CAD
Drone Dev
Robot Arm Kinematics
Git
Linux
Ready to start RDA?
Next cohort: May/June 2026. Physical attendance strongly preferred.
🔗 Dual-Track Programme
Combined AIML + RDA
The Complete AI & Robotics Engineer
This is not AIML followed by RDA. This is a purpose-designed merged curriculum
that uses the overlap between both programmes to go deeper rather than
repeat. Students who take the Combined track graduate as the most versatile
AI and robotics engineers on the continent — capable of building both
the brain and the body of intelligent systems.
💡 Why is this different from doing AIML then RDA?
Back-to-back would mean repeating Python, Computer Vision, NLP, and Neural Networks.
The Combined curriculum merges these into single deeper tracks — so instead of
learning Computer Vision twice, you do it once at full depth combining both
the AI perspective (model training) and the robotics perspective (real-time
embedded deployment). Same knowledge. Greater depth. 24 months well spent.
Merged Curriculum
Semester 1
▾
Hardware Intelligence Foundation
Months 1–6 · Hardware + Python + Data Science together
01
Intro to AI & Robotics + No-Code AI & Prompt Engineering
Full landscape — from neural networks to actuators, from transformers to servo motors. No-code prototyping before writing a line of code.
02
Python — Complete Track
One unified, deep Python track covering: basics, OOP, hardware interfacing, database management, web development, desktop GUI, API integration, and chatbot development. No split between "Python for AI" and "Python for Robotics" — one language, total mastery.
03
Product Design and Development
Design thinking, CAD, 3D printing, prototyping, and product lifecycle — creating both hardware and software products.
04
Microcontroller Fundamentals — Arduino to Advanced
From GPIO to RTOS in one track — stepping up from Arduino to complex multi-sensor embedded systems without a repeat semester.
05
Data Science, Analytics and Database Engineering
pandas, NumPy, SQL, data pipelines, Tableau, Power BI — building the data infrastructure that powers both AI models and robotic control systems.
06
Internet of Things and Industrial Automation
Home automation, industrial IoT, MQTT, cloud connectivity, PLC fundamentals — the bridge between physical hardware and networked intelligence.
07
Practical Electronics and Circuit Design
From Ohm's Law to PCB design — building circuits that underpin every robotic system in the programme.
08
Robot Manufacturing and 3D Design
Mechanical design, CNC machining, robot assembly, actuator selection, and manufacturing precision.
09
Git, GitHub, Cloud, Cybersecurity
Professional software infrastructure: version control, CI/CD, secure APIs, AWS and Azure for both ML models and IoT backends.
Semester 2
▾
Machine Intelligence Systems
Months 7–12 · Pure AI/ML — extended and deepened, not repeated
01
Machine Learning I & II — Unified Deep Track
From supervised learning to reinforcement learning in one continuous track — going deeper than either standalone programme. Includes ensemble methods, XGBoost, and RL for robotics control.
02
Deep Learning and Neural Network Architectures
CNNs, RNNs, LSTMs, Transformers — full architecture study with implementations in both TensorFlow and PyTorch, applied to both AI and robotics problems.
03
Computer Vision — Full Depth (AI + Robotics Track)
OpenCV + deep learning computer vision merged into one programme. Model training, real-time inference on edge devices, YOLO deployment on Raspberry Pi. No repeat — just depth.
04
Natural Language Processing and Large Language Models
From NLP pipelines to fine-tuned LLMs, RAG systems, and voice-controlled robotic interfaces — the language of intelligent systems.
05
Control Systems and PID Control
Feedback control, transfer functions, Laplace transforms, PID tuning — the mathematics and engineering of making things move precisely.
06
Raspberry Pi, ROS and Controller Coordination
Linux, ROS, nodes, topics, services, URDF — the software backbone of every professional robotic system.
07
SLAM and Path Planning
Kalman filters, LiDAR mapping, A*, RRT — how autonomous systems know where they are and how to get where they need to go.
08
Drone System Development
Quadcopter physics, PID for UAVs, autonomous mission planning, computer vision integration for drones.
09
Robot Arm Kinematics and Intelligent Control
Forward and inverse kinematics + reinforcement learning for robot arm control — combining hardware knowledge with advanced AI.
10
AI Deployment, MLOps and Edge Computing
Docker, Kubernetes, TensorFlow Lite, model deployment to embedded systems — running AI on robots in the real world.
Semester 3
▾
Autonomous Systems Integration
Months 13–18 · Unique to Combined — AI meets Robotics
01
Digital Twinning and Simulation
Creating virtual replicas of physical systems — simulating entire AI-powered robot systems before deploying to hardware. Gazebo, RViz, and physics simulation.
02
Reinforcement Learning for Physical Robotic Control
Training robots to learn from their environment using RL — locomotion, manipulation, and adaptive control in physical systems.
03
Intelligent Autonomous Systems
Combining computer vision, NLP, SLAM, and path planning into coherent autonomous systems that perceive, decide, and act.
04
Generative AI and GAN for Robotics
Using GANs for synthetic training data generation, simulation-to-real transfer, and AI-augmented robotic design.
05
AI Ethics, Safety and Responsible Engineering
Bias in robotic systems, fail-safe design, regulatory frameworks for autonomous systems, and the ethics of building machines that make decisions.
06
Research Methods and Technical Writing
Preparing for publication — how to write research papers from your RAIN projects, structure academic arguments, and cite prior work.
07
Pre-Capstone Ideation and Industry Seminars
Friday seminars with industry partners, guest researchers, and alumni. Capstone project definition, scope, and mentor assignment.
Semester 4
▾
Integrated Capstone & Launch
Months 19–24 · The most ambitious projects in the country
01
Integrated AI + Robotics Capstone Project
A full-scale project combining both tracks — e.g. an autonomous medical delivery robot with computer vision and voice control; a smart agricultural drone with crop disease detection; a rehabilitation exoskeleton with adaptive AI control.
02
Hardware + Software Integration and Validation
Building the complete physical + software system. Testing, iteration, fault diagnosis, and performance benchmarking.
03
Research Paper and Publication Pipeline
Refining the capstone into a publishable academic paper or thesis — feeding directly into the B.Sc. degree pathway with our university partner.
04
Portfolio, Startup Ideation and Commercialisation
Building the business case for the capstone project. Product roadmap, market analysis, and pitch deck preparation.
05
Final Demonstration, Certification and Awards
Public demonstration of the capstone project. RAIN certificates issued. Outstanding projects recognised with awards.
Core Technologies — All of the Above
Python
C++
TensorFlow
PyTorch
OpenCV
ROS
Arduino
Raspberry Pi
SLAM
PID
Drone Dev
Robot Arm Kinematics
LLMs
MLOps
AWS / Azure
Docker
3D Printing
IoT
Git
Linux
Ready to do both?
The most comprehensive engineering track in Nigeria. 24 months. Two world-class certifications.
Short Courses — 12–16 Weeks
Focused. Fast. Practical.
RAIN's short courses are drawn from the full programme curricula — giving you a concentrated, intensive introduction to a specific domain. Prerequisites apply.
DSP
Data Science & Python Programming
The complete data science track from AIML Semester 1 — delivered as a standalone
intensive course. You will learn to collect, clean, analyse, visualise, and
model data using Python, pandas, scikit-learn, Tableau, and Power BI.
Graduates leave with a working data pipeline and a portfolio of analytical projects.
Python
pandas
NumPy
scikit-learn
SQL
Matplotlib
Seaborn
Tableau
Power BI
Excel
Apply for DSP
12–16 weeks. Pre-req: O-Level Mathematics credit.
ESIOT
Embedded Systems & Internet of Things
The hardware and IoT track from RDA Semester 1 — delivered standalone.
You will programme microcontrollers, build IoT systems, design circuits,
and deploy connected devices. Students graduate with multiple working
embedded and IoT projects including a home automation system and
an IoT sensor network.
Python
C++
Arduino
Raspberry Pi
IoT / MQTT
Electronics
3D Printing
Git
Apply for ESIOT
12–16 weeks. Physical attendance strongly recommended.
MLAI
Machine Learning / Introduction to AI
Drawn from AIML Semester 2 — an intensive introduction to machine learning
and neural networks for those who already know Python. You will build
classification models, regression pipelines, computer vision systems,
and an NLP application. The fastest path to building AI products
for those with a coding foundation.
Python
scikit-learn
TensorFlow
PyTorch
OpenCV
pandas
NumPy
Hugging Face
Apply for MLAI
12–16 weeks. Pre-req: Python programming certification required.
Investment
Fees & Payment Plans
RAIN fees are payable in instalments — you do not need to pay the full amount upfront. All fees are in Nigerian Naira. A 5% discount applies to full tuition paid in one transfer before the programme starts.
Fee Calculator
Select your programme to see the fee breakdown and instalment plan.
3-Instalment Payment Plan
💡 Accommodation (optional): available on-site at our Ibadan facility — private room, utilities included.
Contact admissions for accommodation fees.
Culture & Community
Life at RAIN
RAIN is not a classroom. It is a research facility, a product lab, a maker space, and a community of serious technologists — all in one building in Ibadan.
The Lab
RAIN's fully equipped robotics and AI laboratory holds everything a serious
engineer needs — robot arms, drones, 3D printers, CNC machines, oscilloscopes,
Arduino and Raspberry Pi kits, LiDAR sensors, depth cameras, high-performance
GPUs, and more. New equipment is constantly added. Students have full
hands-on access from Day 1.
Friday Seminars
Every Friday, the RAIN community gathers for an intensive seminar session.
Students present their projects and receive structured feedback from faculty.
External researchers, industry engineers, and AI practitioners are invited
to present their work. Faculty interrogate and help resolve technical
blockers. This is where the most learning happens.
70% Product Development
RAIN's curriculum is 70% product development by design. Students are
not learning to pass exams — they are building things that work.
Passenger scream detection systems for public buses, exoskeletons for
upper limb rehabilitation, carbon emission tracker drones, smart drug
dispensers for dementia patients. Real problems. Real solutions.
International Community
RAIN attracts students from across Nigeria, West Africa, and beyond.
Some international students have obtained study visas specifically to
train at RAIN in Ibadan. The diversity of perspective in the lab
makes the problem-solving environment richer.
Awards & Recognition
Outstanding projects and trainees are recognised at RAIN's certification
events. The best capstone projects are nominated for external
competitions and showcased publicly. A RAIN award is a meaningful
credential.
On-Site Accommodation
Live and breathe the RAIN experience. Private rooms with en-suite bathrooms,
air conditioning, water heater, unlimited internet, kitchenette, and laundry
— all within the facility. Apply for accommodation as part of your
admission form.
After RAIN
Where RAIN Graduates Go
RAIN does not train you to get a job. RAIN trains you to create jobs. That said, RAIN graduates are among the most sought-after tech talent in Nigeria and West Africa — because they can actually build things.
AI Engineer
Building production AI systems for fintech, healthtech, edtech, and enterprise clients across Nigeria and globally.
Robotics Engineer
Designing and deploying automated systems for manufacturing, logistics, agriculture, and security.
Data Scientist
Turning raw data into actionable intelligence for banks, telecoms, government agencies, and startups.
IoT Developer
Building connected device ecosystems for smart homes, smart factories, and smart cities.
Drone Systems Engineer
Developing autonomous UAVs for surveillance, agriculture, delivery, and environmental monitoring.
AI Research & PhD
Multiple RAIN alumni have been admitted to PhD programmes abroad — on scholarships — in fields they had no prior computer science background in. Their RAIN transcripts opened doors.
Tech Founder
RAIN's 70% product focus means many graduates leave with a product that is already halfway to a startup.
International Employment
RAIN alumni are working in the UK, USA, Canada, and across Europe — in roles that non-RAIN Nigerian graduates simply cannot access.
Alumni
What Our Alumni Are Doing
"The RAIN curriculum is not a shortcut — it is an acceleration. I had no computer science background before RAIN. After, I was admitted into a PhD programme abroad on a full scholarship. My RAIN transcript was the reason."
RAIN Alumni
PhD Candidate — International University (Scholarship)
"I came to RAIN from another country specifically for this programme. Getting the study visa was worth every step. I have since worked on projects in automation and AI that I could not have imagined building before."
International Alumni
Automation Engineer
"RAIN trained me to think in products, not in theory. By the time I graduated, I already had a client for the system I built in my capstone. That is what RAIN does."
RAIN Alumni
Tech Founder
"I did both AIML and RDA. The combination is incomparable. I can now design the hardware, write the control software, and deploy the AI model. There is almost nothing in robotics I cannot build."
Combined Programme Alumni
Senior Robotics Engineer
Admission 2026 is Open.
Your cohort is waiting.
Your cohort is waiting.
Cohort 20 starts May/June 2026. Spaces are limited. Every day without a decision is a day behind.
+234 811 427 6861 · admissions@rainigeria.com · Degree Pathway →