Understand what AI is, its history, and how it differs from ML and deep learning. Master key concepts: training vs inference, supervised/unsupervised/reinforcement learning, overfitting, evaluation metrics, and the data pipeline.

Master the most important ML algorithms: linear and logistic regression, decision trees, random forests, SVMs, KNN, K-Means, PCA, and ensemble methods. Understand regularization, cross-validation, and evaluation metrics in depth.

Build and understand neural networks from perceptrons to Transformers. Learn activation functions, backpropagation, CNNs for vision, RNNs and LSTMs for sequences, the attention mechanism, and how to apply transfer learning with PyTorch or TensorFlow.

Master NLP from the ground up: tokenization, TF-IDF, word embeddings, BERT and GPT architectures, fine-tuning, prompt engineering, RAG, and the capabilities and limitations of modern LLMs.

Understand AI bias, fairness definitions, explainability methods (LIME, SHAP), privacy-preserving techniques, AI safety and alignment, deepfakes, governance frameworks (EU AI Act, NIST RMF), and responsible AI principles.