Each category provides a foundational understanding of the essential terms and concepts in AI.
- General AI Concepts: Covers the basics like AI, Machine Learning, Deep Learning, Natural Language Processing, and Reinforcement Learning.
- Algorithms and Models: Discusses the rules and mathematical representations that power AI, such as algorithms, models, neural networks, decision trees, and Support Vector Machines.
- Data: Explains terms related to the data used in AI, including datasets, training and testing data, features, and labels.
- Evaluation Metrics: Introduces metrics like accuracy, precision, recall, F1 score, and confusion matrix used to evaluate AI models.
- Hardware and Software: Highlights the hardware like CPUs, GPUs, TPUs, and software libraries like TensorFlow and PyTorch used in AI tasks.
- Ethics and Bias: Addresses ethical considerations in AI, including ethical AI, bias, explainability, transparency, and data privacy.
