If you already know the fundamentals of machine learning, this course will help you take the next step into modern AI. Over three days you will explore advanced neural networks, large language models, multimodal AI and MLOps practices that make enterprise deployment possible.
This is not just theory. You will work hands-on with cutting-edge tools and techniques, learning how to fine-tune models, build retrieval-augmented generation systems and manage production AI pipelines with confidence.
Target Audience:
This course is designed for data scientists, machine learning engineers and technical professionals who want to move beyond traditional ML and gain the skills needed to design and deploy advanced AI systems.
Prerequisites:
Participants should have:
- Intermediate Python programming skills
- A basic understanding of machine learning concepts and workflows
- Experience with data manipulation libraries such as Pandas and NumPy
- Familiarity with statistics
- Practical experience building and evaluating ML models
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10 lesson covers:
AI/ML Landscape and Deep Learning Fundamentals
Demystify AI with clear explanations of generative AI, predictive analytics and NLP. Learn how these technologies apply to real world business scenarios, supported by 2025 trends and use cases.
Transfer Learning and Foundation Models
Explore the rise of foundation models and how transfer learning accelerates development. Learn strategies for fine-tuning and embedding extraction, with practical labs on adapting pre-trained models to new domains.
Advanced Neural Network Architectures
Study CNNs for vision tasks, recurrent networks for sequential data and attention-based mechanisms that power today’s most effective models. You will implement CNN-based systems and understand how to select the right architecture for each problem.
Self-Supervised Learning
Discover how to unlock value from unlabelled data using contrastive methods and representation learning. Practical exercises guide you through building a self-supervised pipeline relevant to business data.
Large Language Models (LLMs)
Examine the architecture and optimisation of transformer-based models, techniques for prompt engineering and fine-tuning approaches such as LoRA and QLoRA. You will fine-tune smaller LLMs for domain-specific use cases.
Multimodal AI Architectures
Learn how to combine data types such as text and images within a single model. You will explore alignment techniques, multimodal LLMs and evaluation methods, before building a multimodal system for document analysis.
Retrieval-Augmented Generation (RAG)
Understand how RAG systems combine LLMs with vector databases to enhance accuracy and reliability. Practical labs cover system design, chunking strategies, hybrid search and evaluation metrics.
MLOps Fundamentals
Gain the skills to manage models in enterprise environments. Topics include versioning, CI/CD pipelines, infrastructure scaling and governance. You will build a complete ML pipeline with robust version control.
Model Deployment and Monitoring
Learn how to package, deploy and scale AI models. This module covers containerisation, orchestration, drift detection, retraining frameworks and A/B testing for model updates.
Responsible AI and Ethical Frameworks
Explore bias detection, explainability, regulatory compliance and privacy-preserving machine learning. Labs focus on ethical assessments and governance frameworks for enterprise AI.
