Definition: Machine learning is a subset of artificial intelligence that involves algorithms that learn from data and improve their performance over time.

Detailed Description:

Machine learning algorithms can be categorized into:

• Supervised learning: Algorithms trained on labeled data to predict outcomes.
• Unsupervised learning: Algorithms that find patterns and relationships within unlabeled data.
• Reinforcement learning: Algorithms that learn through trial and error and rewards.

Machine learning is used in various applications, such as:

• Image recognition: Identifying objects, faces, and scenes within images.
• Natural language processing: Understanding and generating human language.
• Recommendation systems: Suggesting products or content based on user preferences.
• Predictive analytics: Forecasting future trends.

Related Terms:

• Artificial Intelligence
• Data Science
• Algorithm
• Model
• Training Data

FAQs:

1. What is the difference between machine learning and artificial intelligence?

   • Machine learning is a subset of artificial intelligence that focuses on algorithms that can learn from data.

2. What are the different types of machine learning?

   • Supervised learning, unsupervised learning, and reinforcement learning.

3. What are some challenges in machine learning?

   • Data quality, overfitting, and interpretability.