What is Machine Learning ?

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Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed for the task. The core idea behind machine learning is to enable computers to learn from data and improve their performance over time.

In traditional programming, humans write explicit instructions for a computer to perform a specific task. In contrast, machine learning algorithms learn patterns and relationships from data, allowing them to generalize and make predictions or decisions on new, unseen data.

There are three main types of machine learning:

Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with corresponding output labels. The algorithm learns the mapping between inputs and outputs, making it capable of predicting the output for new, unseen inputs.

Unsupervised Learning: Unsupervised learning involves working with unlabeled data. The algorithm explores the inherent structure and patterns within the data without explicit guidance. Clustering and dimensionality reduction are common tasks in unsupervised learning.

Reinforcement Learning: Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties, enabling it to learn optimal strategies over time.

Machine learning is applied in various domains, including natural language processing, image and speech recognition, healthcare, finance, and many others. It plays a crucial role in automating complex tasks, making predictions, and extracting insights from large datasets.

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