Machine learning is a subset of artificial intelligence (AI) that focuses on building systems that learn from data. Here are some fundamental concepts in machine learning:
Supervised Learning: This is a type of machine learning where the algorithm learns from labeled training data. The goal is to learn a mapping from input to output, such as predicting house prices based on features like size, location, etc.
Unsupervised Learning: In unsupervised learning, the algorithm is given data without labels. The goal is to find patterns and structure in the data, such as grouping customers into segments based on purchasing behavior.
Reinforcement Learning: This type of learning involves an agent that learns to make decisions by performing actions in an environment to achieve a goal. The agent learns from the consequences of its actions.
For more information on machine learning, you can check out our Machine Learning Tutorial.
Image Recognition: Image recognition is a field of machine learning that involves teaching a computer to identify and classify images. This can be used for a variety of applications, such as facial recognition or identifying objects in images.
Natural Language Processing (NLP): NLP is a field of machine learning that focuses on the interaction between computers and humans using natural language. This can be used for applications such as chatbots or language translation.
- Deep Learning: Deep learning is a subset of machine learning that involves neural networks with many layers. It has been particularly successful in areas such as image and speech recognition.
For a deeper dive into deep learning, you can read our Deep Learning Guide.