- Machine Learning: Understand different types of machine learning such as supervised, unsupervised, and reinforcement learning. It’s important to know how these algorithms learn from data to make predictions or decisions.
- Neural Networks: Grasp the basics of neural networks, including how layers of neurons interact to process information. This includes understanding deep learning, which uses complex neural networks.
- Data Handling: Learn how to preprocess, clean, and split data. Effective data management is key to training models accurately and efficiently.
- Probability and Statistics: Fundamental concepts such as mean, median, standard deviation, and probability distributions are crucial for interpreting model results and making decisions.
- Algorithm Design: Knowledge of how to choose, build, and optimise algorithms based on the problem you are solving. This includes understanding different optimisation techniques such as gradient descent.
- Ethics and Fairness: Understand the ethical implications of AI, including bias, fairness, transparency, and accountability. This is vital to ensure the responsible use of AI technology.
- Computational Thinking: Develop problem-solving skills, including how to break down complex problems into simpler parts, think algorithmically, and use abstraction effectively.
- AI Interpretability and Explainability: Learn techniques to make AI decisions understandable to humans. This is especially important for applications in sensitive areas like healthcare or finance.
- Natural Language Processing (NLP): Understand how machines interpret, process, and generate human language. This includes techniques for text analysis, sentiment analysis, and language generation.
- AI Deployment and Scaling: Know how to deploy AI models effectively, including using cloud services and understanding scalability challenges. This also covers knowledge of frameworks and tools for deploying models into production.