✅ Python basics: syntax, functions, data structures, OOP, file handling.
✅ Math foundations: linear algebra, calculus, probability, statistics.
✅ Data handling: NumPy, Pandas, data preprocessing.
✅ Machine Learning: scikit-learn, supervised/ unsupervised learning, evaluation metrics.
✅ Deep Learning: neural networks, TensorFlow/Keras, CNNs, RNNs, LSTMS.
✅ NLP: text preprocessing, embeddings, sentiment analysis, transformers (intro).
✅ Reinforcement Learning: basics, environments, Q-learning (intro).
✅ Deployment: model saving, APIs with Flask, deployment overview.
✅ Capstone: AI project using real-world data, deep learning, and deployment.
