1. Mathematical basis: Artificial intelligence involves many mathematical concepts and methods, such as linear algebra, probability theory and statistics. These basic mathematical knowledge are very important for understanding and applying artificial intelligence algorithms.
2. Machine learning: Machine learning is one of the core technologies of artificial intelligence. It makes decisions and forecasts by letting computers learn and extract patterns from data. Learning machine learning requires mastering basic concepts and algorithms such as supervised learning, unsupervised learning and reinforcement learning.
3. Deep learning: Deep learning is a branch of machine learning. Learning deep learning requires understanding the basic principles of neural networks, commonly used deep learning frameworks (such as TensorFlow and PyTorch) and related optimization algorithms.
4. Natural language processing: Natural language processing is a field that studies how to make computers understand and process human language. Learning natural language processing requires understanding text processing, semantic analysis, emotional analysis and other technologies.
5. Computer vision: Computer vision is a field that studies how to make computers understand and process images and videos. Learning computer vision requires understanding of image processing, feature extraction, target detection and other technologies.
6. Data science: Data science is a field that studies how to extract valuable information and knowledge from a large amount of data. Learning data science requires understanding data cleaning, data visualization, data analysis and other technologies.