Machine Learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It involves developing algorithms that can process and analyze data to identify patterns, make predictions, or take decisions without human intervention
Types of Machine Learning
Supervised learning
Unsupervised learning
Reinforcement Learning
Semi-supervised learning
Self-Supervised Learning
Online Learning
Batch learning
Transfer Learning
Supervised learning
Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset.The goal is for the model to make accurate predictions or decisions when presented with new, unseen data. Supervised learning is commonly used for tasks such as classification, where the output is a category, and regression, where the output is a continuous value. Examples include spam detection in emails and predicting house prices based on features like size and location.
Unsupervised learning
Unsupervised learning is a type of machine learning where the algorithm is given data without any labels or predefined categories. The goal is to identify patterns, group similar data points, or discover hidden structures within the data. It's commonly used for clustering and association tasks, such as customer segmentation and market basket analysis.
Reinforcement Learning
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative rewards. It involves trial and error, where the agent receives feedback in the form of rewards or penalties.
Semi-supervised learning
Semi-supervised learning is a machine learning approach that uses a small amount of labeled data along with a large amount of unlabeled data for training. It aims to improve learning accuracy by leveraging the unlabeled data to better understand the data structure.
Self-Supervised Learning
Self-supervised learning is a type of machine learning where the system generates its own labels from the input data, allowing it to learn useful representations without explicit human-provided labels. This approach is often used to pre-train models on large datasets before fine-tuning them on specific tasks.
Online Learning
Online learning is a machine learning approach where the model is updated continuously as new data becomes available, rather than being trained on a fixed dataset. This allows the model to adapt quickly to changes and new patterns in the data.
Batch learning
Batch learning is a machine learning approach where the model is trained on a fixed dataset all at once, rather than being updated continuously. This method processes the entire dataset in one go, making it suitable for static data environments.
Transfer Learning
Transfer learning is a machine learning technique where a model developed for a particular task is reused as the starting point for a model on a second task. It leverages the knowledge gained from the first task to improve learning efficiency and performance on the new task.