Artificial Intelligence & Machine Learning

5-Day Professional Training Course | AIML5001

KSA · GCC · Africa

Course Overview

This intensive 5-day training programme equips engineers, data professionals, technology leaders, and analytically minded business practitioners with the theoretical foundations, mathematical principles, programming competencies, and applied implementation skills needed to build, deploy, and manage artificial intelligence and machine learning systems that solve real organisational problems with measurable impact. Where AI Applications courses build awareness and strategic literacy, this programme goes deeper — into the mathematics of learning algorithms, the architecture of neural networks, the engineering discipline of model development, and the operational rigour of production ML systems. Across Saudi Arabia's Vision 2030 AI ecosystem where SDAIA is driving national AI capability development at scale, GCC organisations competing to build indigenous AI engineering talent rather than perpetually importing it, and African technology hubs in Lagos, Nairobi, Cairo, and Cape Town where a generation of AI engineers is emerging to solve distinctly African problems with machine intelligence — the professionals who command genuine AI and machine learning engineering competency are among the most sought-after in any regional talent market. Aligned with the curricula of leading global AI programmes, the MLOps engineering framework, and the practical requirements of AI deployment across industrial, government, and commercial environments in the Middle East and Africa, this programme delivers the rigorous, hands-on AI and machine learning engineering foundation that the profession demands.

Keywords: Artificial Intelligence Machine Learning Training Saudi Arabia | AI Engineering Course GCC | Deep Learning Africa | MLOps Training Riyadh · Dubai · Nairobi · Cairo

Course Information

Target Audience

• Data scientists and ML engineers deepening their theoretical and practical AI competency

• Software engineers and developers transitioning into AI and machine learning roles

• Research engineers in government, academic, and industrial AI programmes across KSA and GCC

• Data analysts ready to move from descriptive analytics into predictive and prescriptive ML

• Engineering professionals implementing AI in industrial, energy, and infrastructure applications

• Technology architects designing AI platform and MLOps infrastructure for organisations

• Startup founders and product engineers building AI-native products across African tech ecosystems

• Academic and research professionals requiring applied ML competency alongside theoretical grounding

Learning Outcomes

Upon successful completion, participants will be able to:

• Implement supervised, unsupervised, and reinforcement learning algorithms from mathematical foundations through Python code to production deployment

• Design, train, evaluate, and optimise deep neural network architectures for classification, regression, computer vision, and natural language processing tasks

• Build end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, evaluation, versioning, and deployment

• Apply MLOps principles and tools to manage the full lifecycle of ML models in production environments

• Evaluate AI systems for bias, fairness, explainability, and responsible deployment in regulated and high-stakes applications

• Navigate the specific AI engineering landscape, compute infrastructure, and talent environment of KSA, GCC, and African technology markets

Learning Methods

5-Day Programme Outline

Day 1 — Mathematical Foundations & Classical Machine Learning

1. The mathematics of machine learning: linear algebra, calculus, probability theory, and information theory — the mathematical toolkit every ML engineer requires, taught through ML applications rather than abstract theory

2. The machine learning framework: hypothesis functions, loss functions, optimisation, gradient descent, and the learning algorithm structure that underlies every ML model from linear regression to transformer networks

3. Supervised learning algorithms: linear and logistic regression, decision trees, support vector machines, k-nearest neighbours — implementation, geometry, and the conditions under which each algorithm performs well or fails

4. Ensemble methods: random forests, gradient boosting, XGBoost, and LightGBM — the algorithms that win most structured data competitions and dominate real-world ML applications in finance, energy, and operations

5. Model evaluation rigour: cross-validation, hyperparameter tuning, regularisation, learning curves, and the experimental discipline that separates ML engineers who build trustworthy models from those who overfit and over-claim

6. Lab session: Participants implement a complete supervised learning pipeline in Python — from raw data through feature engineering, model training, cross-validation, and hyperparameter optimisation to final evaluation

Day 2 — Deep Learning: Neural Network Architecture & Training

1. Neural network foundations: the perceptron, activation functions, forward propagation, backpropagation, and the chain rule — building genuine understanding of how neural networks learn

2. Deep network architectures: depth vs. width, vanishing gradients, batch normalisation, dropout regularisation, and the engineering decisions that determine whether deep networks train successfully or collapse

3. Convolutional Neural Networks (CNNs): convolution operations, pooling, feature map visualisation, and CNN architectures from LeNet to ResNet — the backbone of computer vision AI across industrial inspection, medical imaging, and satellite analysis applications in GCC and Africa

4. Recurrent Neural Networks and LSTMs: sequence modelling, temporal dependencies, and the architectures designed for time series, speech, and natural language data before transformers dominated the field

5. Training infrastructure: GPU computing, mixed precision training, distributed training, and the compute considerations for AI engineering in environments where GPU resources are constrained — a common reality across African AI development contexts

6. Lab session: Participants build and train a CNN image classifier and an LSTM sequence model using TensorFlow and PyTorch — implementing training loops, monitoring convergence, and diagnosing common training failures

Day 3 — Transformers, Large Language Models & Generative AI Engineering

1. The transformer architecture: attention mechanisms, multi-head self-attention, positional encoding, encoder-decoder structure, and the architectural innovation that made large language models possible

2. Pre-trained language models: BERT, GPT, T5, and Llama — the model families, their training objectives, and the conditions under which each architecture is most appropriately applied

3. Fine-tuning and transfer learning: adapting pre-trained models to domain-specific tasks using supervised fine-tuning, RLHF, and parameter-efficient fine-tuning methods including LoRA and QLoRA

4. Retrieval-Augmented Generation (RAG): vector databases, embedding models, semantic search, and the RAG architecture that grounds large language model outputs in organisational knowledge — the most practically valuable LLM application pattern for enterprise deployment

5. Arabic language models and multilingual AI: the specific engineering challenges of building AI systems for Arabic-speaking users across KSA and GCC — dialect variation, morphological complexity, and the models optimised for Arabic NLP performance

6. Lab session: Participants fine-tune a pre-trained language model on a domain-specific dataset and build a basic RAG system connecting an LLM to a document knowledge base

Day 4 — Computer Vision, Reinforcement Learning & Specialised Applications

1. Advanced computer vision: object detection architectures including YOLO and Faster R-CNN, semantic segmentation, instance segmentation, and the visual AI applications transforming infrastructure inspection, safety monitoring, and quality control across GCC and African industrial environments

2. Generative models: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models — the generative architectures behind synthetic data generation, image synthesis, and the rapidly expanding generative AI application landscape

3. Reinforcement learning fundamentals: Markov Decision Processes, Q-learning, policy gradient methods, and the RL applications in robotics, autonomous systems, and process optimisation relevant to GCC industrial automation

4. Graph Neural Networks: graph representation learning, message passing, and GNN applications in molecular property prediction, social network analysis, supply chain optimisation, and fraud detection

5. AI for time series and sensor data: temporal convolutional networks, attention-based forecasting, and anomaly detection architectures for the industrial sensor streams that define AI applications in oil and gas, utilities, and manufacturing

6. Workshop: Participants implement an object detection pipeline using a pre-trained YOLO model and adapt it to a domain-specific detection task relevant to their industry

Day 5 — MLOps, Responsible AI & Production Deployment

1. MLOps engineering: the full ML lifecycle in production — data versioning with DVC, experiment tracking with MLflow, model registry, automated retraining pipelines, and the engineering discipline that keeps ML systems performing reliably after deployment

2. Model serving and deployment: REST APIs with FastAPI, containerisation with Docker, orchestration with Kubernetes, and cloud deployment patterns on AWS SageMaker, Azure ML, and Google Vertex AI

3. Monitoring ML systems in production: data drift detection, model performance degradation, alerting systems, and the operational practices that prevent silent model failure in deployed AI applications

4. AI bias, fairness, and explainability engineering: measuring bias with Fairlearn, generating explanations with SHAP and LIME, and the technical implementation of responsible AI requirements in high-stakes applications across regulated GCC and African environments

5. AI governance and the regulatory landscape: the EU AI Act's technical requirements, Saudi Arabia's AI ethics framework, and the compliance engineering obligations facing organisations deploying AI in regulated industries

6. Capstone: Participants present their complete AI system — covering model architecture, training methodology, evaluation results, deployment pipeline, monitoring strategy, and responsible AI assessment — for peer and facilitator technical review

Regional Relevance

Content is specifically contextualised for AI engineers operating across KSA, GCC, and African technology environments — integrating Saudi Aramco's AI engineering standards, SDAIA's national AI capability development framework, the UAE AI Office's technical deployment requirements, and the AI engineering challenges facing African technology organisations where compute constraints, data scarcity, and multilingual requirements demand engineering ingenuity alongside technical rigour. Industry examples span oil and gas AI, smart city computer vision, African agricultural machine learning, and financial services fraud detection across the full regional technology landscape.

Assessment & Certification

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