This first course is where you will learn how AI works and gain a foundational knowledge of what is involved in AI Engineering. You will learn about different types of AI, including neural networks and large language models, and be introduced to some techniques used for training and fine-tuning models. The course covers essential concepts, including prompt engineering, RAG, vector databases, AI architectures, and foundation models, providing the knowledge needed to understand AI engineering and how modern AI systems work.
This course provides essential knowledge for working with data in AI projects. You will learn about data analysis, different data types and categories, and the complete data workflow from collection through to cleaning. The course provides an understanding of the key skills, knowledge and techniques used to prepare data for AI applications.
In this course, you will learn how to use the essential tools for building AI systems. Which include Jupyter notebooks, which are widely used in data science, machine learning, and AI engineering and VS Code, a powerful code editor developed by Microsoft, that has become one of the most popular tools for software development.
At this stage, you will start your first hands-on project that puts your Python skills into practice by building a car price prediction application. You will develop a complete working app using Python and Streamlit, applying the programming fundamentals you’ve learned to create an interactive tool that predicts vehicle prices. This project gives you critical foundational experience in building real-world AI applications.
This course introduces the essential Python libraries used for data manipulation and visualisation in AI projects. You will learn to work with NumPy for numerical computing, Pandas for data analysis and manipulation, and Matplotlib and Seaborn for creating visualisations. These powerful libraries form the foundation of the Python data science toolkit and are crucial for preparing and exploring data in AI engineering.
This next project challenges you to build a sentiment analysis movie classification application using real-world AI techniques. You will work with Hugging Face and pre-trained models to create a working classifier, learning how to handle evaluation metrics, confusion matrix heatmaps, and error analysis. The project covers security considerations, edge cases, custom data handling, and finetuning techniques, culminating in an interactive Streamlit application complete with a professional readme and Git Hub repository.
This course explores the art and science of prompt engineering for working with large language models. You will learn about prompt engineering and how it is used for AI engineering. The course covers system prompts, APIs, and techniques for developing effective prompts that control output and provide context. You’ll learn about strategies including chain of thought reasoning, few-shot and zero-shot learning, context length, as well as defending against prompt attacks.
This course introduces Retrieval-Augmented Generation (RAG), a powerful technique for enhancing AI applications with external knowledge. You will learn how RAG works, how to build knowledge bases, and explore different RAG architectures. The course covers embeddings, vector databases, and Pinecone, teaching you how to create AI systems that can access and utilise specific information beyond what’s contained in the base model’s training data.
This comprehensive project-based course brings together prompt engineering and RAG to build a complete AI system from front to back end. You will create a working application that uses vector databases and custom knowledge bases to deliver intelligent responses through carefully crafted prompts. The project covers common architectural patterns for AI applications, teaching you how to integrate RAG pipelines with frontend and backend components to create a production-ready system that leverages external knowledge and contextual information.
This course provides a comprehensive introduction to machine learning principles and practices. You will learn what machine learning is, explore ML operations, and dive into key concepts like linear regression and different types of ML algorithms. The course covers essential techniques including scikit-learn for training and testing models, scaling and regularization methods, data processing workflows, and handling outliers and encoding. You’ll gain practical understanding of the challenges in machine learning and the foundational skills needed to build effective ML systems.
This project gets you working on basic machine learning tasks in a practical ML project. You will apply fundamental techniques to help build a working model, gaining hands-on experience with techniques around data preparation, training, and evaluation in a real-world context.
This short course explores the ethical considerations and responsibilities in AI engineering and working with data. You will examine issues like bias in AI systems, data privacy, fairness, transparency, and accountability. The course covers ethical frameworks for decision-making, the societal impact of AI technologies, and best practices for developing AI systems that respect user rights and promote equitable outcomes.
As part of the AI Engineer Job Programme, you will complete a virtual oral exam to assess your understanding of the programme material and your ability to apply what you have learned in practice.
The oral exam forms part of the overall assessment of the programme.
As part of this programme, you will complete the AWS Certified Cloud Practitioner course and exam, which forms the final assessment of the programme.
The AWS Certified Cloud Practitioner course introduces learners to core cloud computing concepts and the fundamentals of Amazon Web Services (AWS).
It is designed to provide a broad understanding of how cloud technologies are used in real-world environments.
You’ve built the skills, completed the projects, and earned your qualifications. Now, it’s time to put it all to work.
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