Artificial intelligence (AI) refers to technology that allows computers and machines to simulate human abilities such as learning, understanding, solving problems, making decisions, being creative, and operating independently.
Machine Learning:
A helpful way to understand artificial intelligence is to see it as a group of related ideas that have developed over many decades. One of the main parts of AI is machine learning.
Machine learning focuses on building models that learn from data. Instead of being programmed step by step for every task, these models are trained to recognize patterns and make predictions or decisions on their own. It includes many different methods such as linear regression, decision trees, random forests, support vector machines, and clustering, each suited for different types of problems.

One of the most widely used approaches is neural networks. These are inspired by how the human brain works, using layers of connected nodes to process information. They are especially good at finding complex patterns in large datasets.

A basic type of machine learning is supervised learning, where the model is trained using labeled data. This means each input comes with the correct answer, helping the model learn how to map inputs to outputs and make accurate predictions on new data.

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Deep Learning
Deep learning is a more advanced part of machine learning that uses neural networks with many layers, called deep neural networks. These layers allow the system to better mimic how the human brain processes information.

Unlike simpler models, deep neural networks have multiple hidden layers between input and output, which helps them understand complex data more effectively. They can even learn from data without labels, automatically identifying important features.

Because deep learning requires less human involvement, it can handle very large and complex datasets. It is commonly used in areas like language processing and image recognition, and it powers many modern AI applications.

Deep learning also supports different learning approaches, such as:

Transfer learning: applies knowledge from one task to improve another

Semi-supervised learning: uses both labeled and unlabeled data

Self-supervised learning: creates its own labels from raw data

Reinforcement learning: learns through trial and error with rewards