James Harvey - Computer Science Student & Software Developer

Languages

• • JavaScript / TypeScript
• • Python
• • Java
• • HTML / CSS
• • SQL
• • JSON

Frameworks & Tools

• • Node.js
• • Git / GitHub / GitLab
• • VS Code
• • P5.js
• • pygame

Core CS

• • Data structures & algorithms
• • OOP + clean code
• • Testing basics
• • Problem solving
• • Principles Techniques and Tools for Secure Software Engineering
• • Computational Thinking
• • Discrete Mathematics
• • Web Applications

• • Covered Artificial Intelligence foundations: core AI concepts, tasks and data, and a clear comparison of Artificial Intelligence (AI) vs Machine Learning (ML) vs Deep Learning (DL).
• • Machine learning foundations: supervised learning (regression and classification), unsupervised learning, and reinforcement learning (RL), with practical demonstrations using Jupyter Notebooks.
• • Deep learning foundations: convolutional neural networks (CNNs) and sequence models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), including a multilayer perceptron (MLP) classification demo.
• • Generative Artificial Intelligence and Large Language Model foundations (GenAI & LLMs): introduction to generative AI, large language models, transformer architectures, prompt engineering, and customising LLMs with domain-specific data.
• • Oracle Cloud Infrastructure Artificial Intelligence portfolio: overview of OCI AI and ML services and infrastructure, including graphics processing units (GPUs), superclusters, and responsible AI considerations.
• • Oracle Cloud Infrastructure Generative AI Service: OCI Generative AI, vector search, and Oracle Select AI, supported by hands-on demonstrations.
• • Oracle Cloud Infrastructure Artificial Intelligence Services: computer vision, speech recognition, natural language processing, and document understanding services, including practical demonstrations.

Result: Oracle Cloud Infrastructure AI Associate

• • Differentiated between Artificial Intelligence and key subcategories, including generative AI.
• • Explored how generative AI systems work, including natural language models and content generation workflows.
• • Learned the elements involved in creating content with AI tools.
• • Gained an introduction to variational autoencoders (VAEs) and their use in anomaly detection.
• • Considered future trends, ethical implications, and how organisations and leaders can implement AI effectively.