Each category provides a foundational understanding of the essential terms and concepts in AI.

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