What is ml in a shot?

Machine Learning in a Nutshell

Machine Learning (ML) is a subfield of artificial intelligence that focuses on enabling computers to learn from data without being explicitly programmed. Instead of relying on pre-defined rules, ML algorithms identify patterns, make predictions, and improve their performance over time through experience.

Key Concepts:

• Algorithms: The core of ML lies in algorithms that analyze data and learn from it. Common types include linear regression, decision trees, support vector machines (SVMs), and neural networks.
• Data: ML algorithms thrive on data. The quality and quantity of data directly impact the accuracy and reliability of the learned models.
• Training: This process involves feeding data to an algorithm, allowing it to adjust its internal parameters to minimize errors and improve its predictive capabilities.
• Models: The output of the training process is a model, which represents the learned relationships and patterns in the data. This model can then be used to make predictions or decisions on new, unseen data.
• Features: These are the individual measurable properties or characteristics of the data that the algorithm uses to learn.
• Supervised Learning: Algorithms learn from labeled data, where the correct output is known for each input.
• Unsupervised Learning: Algorithms learn from unlabeled data, discovering hidden patterns and structures without explicit guidance.
• Reinforcement Learning: An agent learns to make decisions in an environment to maximize a reward.

Applications:

ML is used in a wide range of applications, including:

• Image recognition
• Natural language processing
• Recommendation systems
• Fraud detection
• Medical diagnosis
• Autonomous driving