Machine Learning (ML) Fundamentals:
- Supervised Learning: Training models on labeled data to make predictions on new, unseen data.
- Unsupervised Learning: Discovering patterns and relationships in unlabeled data.
- Reinforcement Learning: Training models to make decisions by interacting with an environment and receiving rewards or penalties.
- Deep Learning: A subset of ML that uses neural networks with multiple layers to learn complex representations.
- Linear Algebra: Understanding concepts like vectors, matrices, and tensor operations.