Machine Learning Details

Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on developing systems that learn—or improve performance—based on the data they ingest. Artificial intelligence is a broad word that refers to systems or machines that resemble human intelligence. Machine learning and AI are frequently discussed together, and the terms are occasionally used interchangeably, although they do not signify the same thing. A crucial distinction is that, while all machine learning is AI, not all AI is machine learning.

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. IBM has a rich history with machine learning.

Types of Machine Learning:

• Supervised Learning: This is like the animal image recognition example. The data is labeled, and the model learns to map the inputs (image) to the desired outputs (animal type).
• Unsupervised Learning: Here, the data is unlabeled. The model finds hidden patterns and structures within the data itself. For instance, an unsupervised algorithm might group customers into different segments based on their purchase history.
• Reinforcement Learning: This is where the machine learning model learns through trial and error by interacting with an environment. A classic example is AlphaGo, the AI program that defeated professional Go players by learning from millions of gameplay scenarios.

 4 Basics of Machine Learning

·       Supervised learning: (also called inductive learning) Training data includes desired outputs.

·       Unsupervised learning: Training data does not include desired outputs.

·       Semi-supervised learning: Training data includes a few desired outputs.

·       Reinforcement learning: Rewards from a sequence of actions.

Articles on Machine Learning