Machine Learning Fundamentals

Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.

There are three main types of machine learning:

Supervised Learning: The algorithm learns from labeled training data. Examples include classification and regression problems. Common algorithms are linear regression, decision trees, and neural networks.

Unsupervised Learning: The algorithm finds patterns in unlabeled data. Clustering is a common application. K-means and hierarchical clustering are popular methods.

Reinforcement Learning: An agent learns by interacting with an environment and receiving rewards or penalties. This approach is used in game playing, robotics, and recommendation systems.

Deep learning is a subset of machine learning that uses neural networks with many layers. It has achieved remarkable results in image recognition, natural language processing, and speech recognition.

The key steps in a machine learning project are:
1. Problem definition
2. Data collection and preparation
3. Feature engineering
4. Model selection and training
5. Evaluation and tuning
6. Deployment and monitoring

Data quality is crucial for machine learning success. Garbage in, garbage out applies strongly to ML systems.
