Machine learning is a subfield of artificial intelligence that focuses on developing systems that can learn from and make decisions based on data. Unlike traditional programming where explicit instructions are provided, machine learning algorithms build models based on sample data to make predictions or decisions without being explicitly programmed to do so.

Key concepts in machine learning include:

1. Supervised Learning: The algorithm learns from labeled training data, making predictions or decisions based on that data.
2. Unsupervised Learning: The algorithm learns patterns from unlabeled data.
3. Reinforcement Learning: The algorithm learns by interacting with an environment, receiving rewards or penalties.
4. Deep Learning: A subset of machine learning using neural networks with many layers.

Machine learning has numerous applications across industries, including:
- Healthcare: Disease diagnosis, personalized treatment plans
- Finance: Fraud detection, algorithmic trading
- Transportation: Self-driving vehicles, traffic prediction
- Marketing: Customer segmentation, recommendation systems
- Manufacturing: Predictive maintenance, quality control

As the field continues to evolve, ethical considerations around bias, privacy, and transparency have become increasingly important. 