To understand Artificial Intelligence (AI), you can start by studying the following topics:

Machine Learning – a subfield of AI that focuses on algorithms that can learn from data and make predictions or take actions.

Neural Networks – a type of machine learning model that is inspired by the structure and function of the human brain.

Deep Learning – a subfield of machine learning that uses deep neural networks to solve complex problems.

Natural Language Processing (NLP) – a branch of AI that focuses on enabling computers to understand, interpret, and generate human language.

Computer Vision – a field of AI concerned with enabling computers to understand and interpret visual data from the world, such as images and videos.

Robotics – a field of AI that deals with the design, construction, and operation of robots.

Reinforcement Learning – a type of machine learning where an agent learns to make decisions by performing actions in an environment and receiving feedback in the form of rewards or penalties.

Ethics and Bias in AI – the study of the ethical and social implications of AI, including issues related to fairness, transparency, accountability, and bias in AI systems.

These topics cover the fundamentals of AI and provide a solid foundation for further exploration and learning.