"ML" typically refers to "Machine Learning," which is a subfield of artificial intelligence (AI). Machine Learning is a set of techniques and algorithms that enable computers to learn from data and make predictions or decisions without being explicitly programmed for the task. In other words, instead of relying on explicit programming, machine learning systems use statistical patterns and inference to improve their performance over time.
There are several types of machine learning approaches, including:
- Supervised Learning: In this type of learning, the algorithm is trained on a labeled dataset, where the input data is paired with the corresponding output or target. The algorithm learns to map the input data to the correct output by generalizing patterns from the training data. Common tasks include classification and regression.
- Unsupervised Learning: Unsupervised learning involves training the algorithm on an unlabeled dataset, and the system tries to find patterns or relationships within the data without explicit guidance. Clustering and dimensionality reduction are examples of unsupervised learning tasks.
- Reinforcement Learning: In reinforcement learning, an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on the actions it takes, and it learns to maximize cumulative rewards over time.
- Semi-Supervised Learning: This approach combines elements of both supervised and unsupervised learning. The model is trained on a dataset that contains both labeled and unlabeled data. It uses the labeled data to learn patterns and can generalize to make predictions on new, unseen data.
- Deep Learning: Deep learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). Deep learning has been particularly successful in tasks such as image and speech recognition, natural language processing, and more.
